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Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education. This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods. A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users' demographics by matching predefined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI. The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the \"broadcasting\" group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the \"scientific\" group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification. This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.

Indigenous Canadians experience a disproportionate burden of chronic atherosclerotic diseases, including peripheral artery disease (PAD). Despite an estimated prevalence of 800 000 patients with PAD in Canada, the burden of the disease among Indigenous Canadians is unclear. Available evidence suggests that this population has a higher prevalence of several major risk factors associated with PAD (diabetes, smoking and kidney disease). Unique socioeconomic, geographic and systemic obstacles affecting Indigenous Canadians’ health and health care access may worsen chronic disease outcomes. Little is known about the cardiovascular and limb outcomes of Indigenous peoples with PAD. A novel approach via multidisciplinary vascular health teams engaging Indigenous communities in a culturally competent manner may potentially provide optimal vascular care to this population. Further research into the prevalence and outcomes of PAD among Indigenous Canadians is necessary to define the problem and allow development of more effective initiatives to alleviate the disease burden in this marginalized group. Au Canada, les membres des communautés autochtones sont affectés de manière disproportionnée par les maladies athéroscléreuses chroniques, y compris par l’artériopathie périphérique (AP). Malgré une prévalence estimée de 800 000 patients atteints d’AP au Canada, on ignore quel est le fardeau de la maladie chez les membres des communautés autochtones. Selon les données accessibles, cette population présente une prévalence plus élevée de plusieurs facteurs de risque majeurs associé à l’AP (diabète, tabagisme et maladie rénale). Certains obstacles socioéconomiques, géographiques et systémiques particuliers nuisent aussi à leur santé et leur compliquent l’accès aux soins de santé, ce qui pourrait aggraver les répercussions des maladies chroniques. On en sait peu sur l’issue des problèmes cardiovasculaires et circulatoires périphériques chez les membres des communautés autochtones touchés par l’AP. Une approche nouvelle, impliquant les communautés concernées et offerte de manière culturellement compétente par des équipes de santé vasculaire multidisciplinaires, serait propice à la prestation de soins vasculaires optimaux. Il faudra approfondir la recherche sur la prévalence et l’issue de l’AP chez les membres des communautés autochtones pour cerner le problème et permettre la mise en place d’initiatives plus efficaces afin d’alléger le fardeau de la maladie dans ce groupe marginalisé.

Aspirin is a key antiplatelet therapy for the prevention of thrombotic events in patients with cardiovascular disease. Studies suggest that ≈20% of patients with cardiac disease suffer from aspirin nonsensitivity, a phenomenon characterized by the inability of 81 mg aspirin to inhibit platelet aggregation and/or prevent adverse cardiovascular events. To investigate aspirin nonsensitivity in patients with vascular disease and assess the consequences of aspirin nonsensitivity. One hundred fifty patients presenting to St. Michael’s Hospital’s outpatient clinics with evidence of vascular disease (peripheral arterial disease or carotid artery stenosis) and a previous prescription of 81 mg of aspirin were recruited in this study. Light transmission aggregometry with arachidonic acid induction was used to determine sensitivity to aspirin. Patients with a maximum aggregation ≥20% in response to arachidonic acid were considered aspirin nonsensitive, as per previous studies. Of the 150 patients recruited, 36 patients (24%) were nonsensitive to 81 mg of aspirin. Of these 36 nonsensitive patients, 30 patients provided a urine sample for urine salicyluric acid analysis (a major metabolite of aspirin). Urine analysis demonstrated that 14 patients were compliant and 16 were noncompliant with their aspirin therapy. Major adverse cardiovascular events and major adverse limb events were significantly higher in the nonsensitive patients compared to sensitive patients (hazard ratio, 3.68; P< 0.001). These data highlight the high prevalence of aspirin nonsensitivity and noncompliance in patients with vascular disease and emphasizes the urgent need for improved medical management options for this patient population.

Summary The physiological role of autophagic flux within the vascular endothelial layer remains poorly understood. Here, we show that in primary endothelial cells, oxidized and native LDL stimulates autophagosome formation. Moreover, by both confocal and electron microscopy, excess native or modified LDL appears to be engulfed within autophagic structures. Transient knockdown of the essential autophagy gene ATG7 resulted in higher levels of intracellular 125I-LDL and oxidized LDL (OxLDL) accumulation, suggesting that in endothelial cells, autophagy may represent an important mechanism to regulate excess, exogenous lipids. The physiological importance of these observations was assessed using mice containing a conditional deletion of ATG7 within the endothelium. Following acute intravenous infusion of fluorescently labeled OxLDL, mice lacking endothelial expression of ATG7 demonstrated prolonged retention of OxLDL within the retinal pigment epithelium (RPE) and choroidal endothelium of the eye. In a chronic model of lipid excess, we analyzed atherosclerotic burden in ApoE-/-mice with or without endothelial autophagic flux. The absence of endothelial autophagy markedly increased atherosclerotic burden. Thus, in both an acute and chronic in vivo model, endothelial autophagy appears critically important in limiting lipid accumulation within the vessel wall. As such, strategies that stimulate autophagy, or prevent the age-dependent decline in autophagic flux, might be particularly beneficial in treating atherosclerotic vascular disease.

ObjectiveMany studies have analysed gender bias in academic medicine; however, no comprehensive synthesis of the literature has been performed. We conducted a pooled analysis of the difference in the proportion of men versus women with full professorship among academic physicians.DesignSystematic review and meta-analysis.Data sourcesMEDLINE, Embase, Cochrane Central Register of Controlled Trials, Education Resources Information Center and PsycINFO were searched from inception to 3 July 2020.Study selectionAll original studies reporting faculty rank stratified by gender worldwide were included.Data extraction and synthesisStudy screening, data extraction and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. Meta-analysis was conducted using random-effects models.ResultsOur search yielded 5897 articles. 218 studies were included with 991 207 academic physician data points. Men were 2.77 times more likely to be full professors (182 271/643 790 men vs 30 349/251 501 women, OR 2.77, 95% CI 2.57 to 2.98). Although men practised for longer (median 18 vs 12 years, p<0.00002), the gender gap remained after pooling seven studies that adjusted for factors including time in practice, specialty, publications, h-index, additional PhD and institution (adjusted OR 1.83, 95% CI 1.04 to 3.20). Meta-regression by data collection year demonstrated improvement over time (p=0.0011); however, subgroup analysis showed that gender disparities remain significant in the 2010–2020 decade (OR 2.63, 95% CI 2.48 to 2.80). The gender gap was present across all specialties and both within and outside of North America. Men published more papers (mean difference 17.2, 95% CI 14.7 to 19.7), earned higher salaries (mean difference $33 256, 95% CI $25 969 to $40 542) and were more likely to be departmental chairs (OR 2.61, 95% CI 2.19 to 3.12).ConclusionsGender inequity in academic medicine exists across all specialties, geographical regions and multiple measures of success, including academic rank, publications, salary and leadership. Men are more likely than women to be full professors after controlling for experience, academic productivity and specialty. Although there has been some improvement over time, the gender disparity in faculty rank persists.PROSPERO registration numberCRD42020197414.

The evolving clinical burden of limb loss secondary to diabetes and peripheral artery disease remains poorly characterized. We sought to examine secular trends in the rate of lower-extremity amputations related to diabetes, peripheral artery disease or both. We included all individuals aged 40 years and older who underwent lower-extremity amputations related to diabetes or peripheral artery disease in Ontario, Canada (2005–2016). We identified patients and amputations through deterministic linkage of administrative health databases. Quarterly rates (per 100 000 individuals aged ≥ 40 yr) of any (major or minor) amputation and of major amputations alone were calculated. We used time-series analyses with exponential smoothing models to characterize secular trends and forecast 2 years forward in time. A total of 20 062 patients underwent any lower-extremity amputation, of which 12 786 (63.7%) underwent a major (above ankle) amputation. Diabetes was present in 81.8%, peripheral artery disease in 93.8%, and both diabetes and peripheral artery disease in 75.6%. The rate of any amputation initially declined from 9.88 to 8.62 per 100 000 between Q2 of 2005 and Q4 of 2010, but increased again by Q1 of 2016 to 10.0 per 100 000 (p = 0.003). We observed a significant increase in the rate of any amputation among patients with diabetes, peripheral artery disease, and both diabetes and peripheral artery disease. Major amputations did not significantly change among patients with diabetes, peripheral artery disease or both. Lower-extremity amputations related to diabetes, peripheral artery disease or both have increased over the last decade. These data support renewed efforts to prevent and decrease the burden of limb loss.

Patients undergoing open surgical repair of abdominal aortic aneurysm (AAA) have a high risk of post-operative complications. However, there are no widely used tools to predict surgical risk in this population. We used machine learning (ML) techniques to develop automated algorithms that predict 30-day outcomes following open AAA repair. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent elective, non-ruptured open AAA repair between 2011 and 2021. Input features included 35 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death). We split our data into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 ML models were trained using pre-operative features with logistic regression as the baseline comparator. Overall, 3,620 patients were included. Thirty-day MACE occurred in 311 (8.6%) patients. The best performing prediction model was XGBoost, achieving an AUROC (95% CI) of 0.90 (0.89–0.91). Comparatively, logistic regression had an AUROC (95% CI) of 0.66 (0.64–0.68). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.03. Our automated ML algorithm can help guide risk-mitigation strategies for patients being considered for open AAA repair to improve outcomes.

Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991–1996) to 118 (2016–2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61–1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.

Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complications. Risk prediction tools may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year outcomes following TCAR. The Vascular Quality Initiative (VQI) database was used to identify patients who underwent TCAR between 2016 and 2023. We identified 115 features from the index hospitalization (82 pre-operative [demographic/clinical], 14 intra-operative [procedural], and 19 post-operative [in-hospital course/complications]). The primary outcome was 1-year post-procedural stroke or death. The data was divided into training (70%) and test (30%) sets. Six ML models were trained using pre-operative features with tenfold cross-validation. Overall, 38,325 patients were included (mean age 73.1 [SD 9.0] years, 14,248 [37.2%] female) and 2,672 (7.0%) developed 1-year stroke or death. The best pre-operative prediction model was XGBoost, achieving an AUROC of 0.91 (95% CI 0.90–0.92). In comparison, logistic regression had an AUROC of 0.68 (95% CI 0.66–0.70). The XGBoost model maintained excellent performance at the intra- and post-operative stages, with AUROC’s (95% CI’s) of 0.92 (0.91–0.93) and 0.94 (0.93–0.95), respectively. Our ML algorithm has potential for important utility in guiding peri-operative risk-mitigation strategies to prevent adverse outcomes following TCAR.