Determinants of Mortality and Mid-Term Outcomes After Transcarotid Artery Revascularization and Transfemoral Carotid Artery Stenting.

OBJECTIVES: The potential benefit of transcarotid artery revascularization (TCAR) over transfemoral carotid artery stenting (tfCAS) has been studied in the perioperative period with lower rates of stroke and death; however, data on mid-term outcomes are limited. We aimed to evaluate 3-year outcomes after TCAR and tfCAS and determine the primary predictors of 30-day and 1-year mortality following TCAR. METHODS: Data from the Vascular Quality Initiative for patients undergoing TCAR or tfCAS from January 2016 to December 2022 were analyzed. 1:1 propensity score matching using the nearest-neighbor method was used to adjust baseline demographics and clinical characteristics. Kaplan-Meier survival analysis and Cox Proportional Hazard Regression were used to evaluate long-term outcomes. Iterative stepwise multiple logistic regression analysis and Cox Proportional Hazard Regression were used to identify predictors of 30-day and 1-year mortality, respectively, based upon preoperative, intraoperative, and postoperative factors. RESULTS: A total of 70 237 patients were included in analysis (TCAR=58.7%, tfCAS=41.3%). Transcarotid artery revascularization patients were older and had higher rates of comorbid conditions and high-risk medical and anatomic features than tfCAS patients. Propensity score matching yielded 22 322 pairs with no major differences between groups except that TCAR patients were older (71.6 years vs 70.8 years). At 3 years, TCAR was associated with a 24% reduction in hazard of death compared with tfCAS (hazard ratio [HR]=0.76, 95% confidence interval [CI]=0.71-0.82, p<0.001), for both symptomatic and asymptomatic patients. This survival advantage was established in the first 6 months (HR=0.59, 95% CI=0.53-0.62, p<0.001), with no difference in mortality risk from 6 months to 36 months (HR=0.95, 95% CI=0.86-1.05, p=0.31). Transcarotid artery revascularization was also associated with decreased hazard for 3-year stroke (HR=0.81, 95% CI=0.66-0.99, p=0.04) and stroke or death (HR=0.81, 95% CI=0.76-0.87, p<0.001) compared with tfCAS. The top predictors for 30-day and 1-year mortality were postoperative complications. The primary independent predictor was the occurrence of postoperative stroke. CONCLUSIONS: Transcarotid artery revascularization had a sustained mid-term survival advantage associated over tfCAS, with the benefit being established primarily within the first 6 months. Notably, our findings highlight the importance of postoperative stroke as the primary independent predictor for 30-day and 1-year mortal. CLINICAL IMPACT: The ongoing debate over the superiority of TCAR compared to tfCAS and CEA has been limited by a lack of comparative studies examining the impact of pre-operative symptoms on outcomes. Furthermore, data are scarce on mid-term outcomes for TCAR beyond the perioperative period. As a result, it remains uncertain whether the initial benefits of stroke and death reduction observed with TCAR over tfCAS persist beyond one year. Our study addresses these gaps in the literature, offering evidence to enable clinicians to assess the efficacy of TCAR for up to three years. Additionally, our study seeks to identify risk factors for postoperative mortality following TCAR, facilitating optimal patient stratification.

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges.

Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

Local aortic aneurysm wall expansion measured with automated image analysis.

BACKGROUND: Assessment of regional aortic wall deformation (RAWD) might better predict for abdominal aortic aneurysm (AAA) rupture than the maximal aortic diameter or growth rate. Using sequential computed tomography angiograms (CTAs), we developed a streamlined, semiautomated method of computing RAWD using deformable image registration (dirRAWD). METHODS: Paired sequential CTAs performed 1 to 2 years apart of 15 patients with AAAs of various shapes and sizes were selected. Using each patient's initial CTA, the luminal and aortic wall surfaces were segmented both manually and semiautomatically. Next, the same patient's follow-up CTA was aligned with the first using automated rigid image registration. Deformable image registration was then used to calculate the local aneurysm wall expansion between the sequential scans (dirRAWD). To measure technique accuracy, the deformable registration results were compared with the local displacement of anatomic landmarks (fiducial markers), such as the origin of the inferior mesenteric artery and/or aortic wall calcifications. Additionally, for each patient, the maximal RAWD was manually measured for each aneurysm and was compared with the dirRAWD at the same location. RESULTS: The technique was successful in all patients. The mean landmark displacement error was 0.59 ± 0.93 mm with no difference between true landmark displacement and deformable registration landmark displacement by Wilcoxon rank sum test (P = .39). The absolute difference between the manually measured maximal RAWD and dirRAWD was 0.27 ± 0.23 mm, with a relative difference of 7.9% and no difference using the Wilcoxon rank sum test (P = .69). No differences were found in the maximal dirRAWD when derived using a purely manual AAA segmentation compared with using semiautomated AAA segmentation (P = .55). CONCLUSIONS: We found accurate and automated RAWD measurements were feasible with clinically insignificant errors. Using semiautomated AAA segmentations for deformable image registration methods did not alter maximal dirRAWD accuracy compared with using manual AAA segmentations. Future work will compare dirRAWD with finite element analysis-derived regional wall stress and determine whether dirRAWD might serve as an independent predictor of rupture risk.

Molecular Interaction Between Butorphanol and κ-Opioid Receptor.

BACKGROUND: The misuse of opioids stems, in part, from inadequate knowledge of molecular interactions between opioids and opioid receptors. It is still unclear why some opioids are far more addictive than others. The κ-opioid receptor (KOR) plays a critical role in modulating pain, addiction, and many other physiological and pathological processes. Butorphanol, an opioid analgesic, is a less addictive opioid with unique pharmacological profiles. In this study, we investigated the interaction between butorphanol and KOR to obtain insights into the safe usage of this medication. METHODS: We determined the binding affinity of butorphanol to KOR with a naltrexone competition study. Recombinant KORs expressed in mammalian cell membranes (Chem-1) were used for G-protein activation studies, and a human embryonic kidney-293 (HEK-293) cell line stably transfected with the human KOR was used for ß-arrestin study as previously described in the literature. The effects of butorphanol on KOR internalization were investigated using mouse neuroblastoma Neuro2A cells stably transfected with mKOR-tdTomato fusion protein (N2A-mKOR-tdT) cells overexpressing KOR. The active-state KOR crystal structure was used for docking calculation of butorphanol to characterize the ligand binding site. Salvinorin A, a full KOR agonist, was used as a control for comparison. RESULTS: The affinity of KOR for butorphanol is characterized by Kd of 0.1 ± 0.02 nM, about 20-fold higher compared with that of the µ-opioid receptor (MOR; 2.4 ± 1.2 nM). Our data indicate that butorphanol is more potent on KOR than on MOR. In addition, butorphanol acts as a partial agonist of KOR in the G-protein activation pathway and is a full agonist on the ß-arrestin recruitment pathway, similar to that of salvinorin A. The activation of the ß-arrestin pathway is further confirmed by KOR internalization. The in silico docking model indicates that both salvinorin A and butorphanol share the same binding cavity with the KOR full agonist MP1104. This cavity plays an important role in determining either agonist or antagonist effects of the ligand. CONCLUSIONS: In conclusion, butorphanol is a partial KOR agonist in the G-protein activation pathway and a potent KOR full agonist in the ß-arrestin recruitment pathway. The structure analysis offers insights into the molecular mechanism of KOR interaction and activation by butorphanol.

Middle School Students Effectively Improve Stroke Knowledge and Pass Them to Family Members in China Using Stroke 1-2-0.

Background and Purpose: This study tests the hypothesis that middle school and high school students can improve their stroke knowledge using Stroke 1-2-0, a stroke educational tool, and pass this knowledge on to their family members. Methods: A total of 625 students and 198 parents/grandparents participated in learning about stroke using Stroke 1-2-0. After a group training session for the students by a neurologist at school, the students took educational material to home and educated their parents/grandparents. A questionnaire was given to students, parents/grandparents before, immediately after, and 1 year after the educational event. Results: All participants agreed that Stroke 1-2-0 was a much easier tool to remember than FAST. Almost all the students (96.4%) remembered the meaning of Stroke 1-2-0 as compared to 7.3% from the base line (p < 0.001). The rate of complete Stroke 1-2-0 mastery from 96.3% fell to 84.4% at 3 months and 63.8% at 1 year after training (p < 0.001). Following education from children, the proportion of parents/grandparents who mastered Stroke 1-2-0 was significantly higher than baseline (79.9 vs. 24.8%). Conclusion: Middle school and high school students can effectively use Stroke 1-2-0 to improve their stroke knowledge and pass this knowledge to their family members. Sustained educational efforts and repeated educational events are needed though.

Too Young to Have a Stroke?-a Global Health Crisis.

This editorial discusses the importance of improving awareness of stroke in young individuals. Stoke can occur in any age group and is not restricted to elderly populations. Today, the average age of the first-time stroke patient continues to decrease. However, the incidence of stroke in seemingly healthy, young adults remains neglected, and stroke awareness among young patients remains poor, even in well-developed countries. Education targeting two common barriers to stroke care, identification and rescue, should be implemented for both medical professionals and the public domain. Only through education can we reduce preventable stroke-related death and damage in young patients moving forward.

pH Sensing Properties of Flexible, Bias-Free Graphene Microelectrodes in Complex Fluids: From Phosphate Buffer Solution to Human Serum.

Advances in techniques for monitoring pH in complex fluids can have a significant impact on analytical and biomedical applications. This study develops flexible graphene microelectrodes (GEs) for rapid (<5 s), very-low-power (femtowatt) detection of the pH of complex biofluids by measuring real-time Faradaic charge transfer between the GE and a solution at zero electrical bias. For an idealized sample of phosphate buffer solution (PBS), the Faradaic current is varied monotonically and systematically with the pH, with a resolution of ≈0.2 pH unit. The current-pH dependence is well described by a hybrid analytical-computational model, where the electric double layer derives from an intrinsic, pH-independent (positive) charge associated with the graphene-water interface and ionizable (negative) charged groups. For ferritin solution, the relative Faradaic current, defined as the difference between the measured current response and a baseline response due to PBS, shows a strong signal associated with ferritin disassembly and the release of ferric ions at pH ≈2.0. For samples of human serum, the Faradaic current shows a reproducible rapid (<20 s) response to pH. By combining the Faradaic current and real-time current variation, the methodology is potentially suitable for use to detect tumor-induced changes in extracellular pH.

Scalable Production of High-Sensitivity, Label-Free DNA Biosensors Based on Back-Gated Graphene Field Effect Transistors.

Scalable production of all-electronic DNA biosensors with high sensitivity and selectivity is a critical enabling step for research and applications associated with detection of DNA hybridization. We have developed a scalable and very reproducible (>90% yield) fabrication process for label-free DNA biosensors based upon graphene field effect transistors (GFETs) functionalized with single-stranded probe DNA. The shift of the GFET sensor Dirac point voltage varied systematically with the concentration of target DNA. The biosensors demonstrated a broad analytical range and limit of detection of 1 fM for 60-mer DNA oligonucleotide. In control experiments with mismatched DNA oligomers, the impact of the mismatch position on the DNA hybridization strength was confirmed. This class of highly sensitive DNA biosensors offers the prospect of detection of DNA hybridization and sequencing in a rapid, inexpensive, and accurate way.