Comprehensive review of virtual assistants in vascular surgery.

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants. These tools may support holistic, multidisciplinary, and high-quality vascular care delivery throughout the pre-, intra-, and postoperative stages. Importantly, it is critical to consider the design, safety, and challenges related to virtual assistants, including data security, ethical, and equity concerns. By combining the perspectives of patients, clinicians, data scientists, and other stakeholders when developing, implementing, and monitoring virtual assistants, there is potential to harness the power of this technology to care for vascular surgery patients more effectively. In this comprehensive review article, we introduce the concept of virtual assistants, describe potential applications of virtual assistants in vascular surgery for clinicians and patients, highlight the benefits and drawbacks of large language models, such as GPT-4, and discuss considerations around the design, safety, and challenges associated with virtual assistants in vascular surgery.

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS. METHODS AND RESULTS: The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative). CONCLUSIONS: Machine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Predicting inferior vena cava filter complications using machine learning.

OBJECTIVE: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using preoperative features with 10-fold cross-validation (Extreme Gradient Boosting, random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement. RESULTS: Overall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 ± 16.7 years vs 63.8 ± 16.0 years; P < .001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was Extreme Gradient Boosting, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). In comparison, logistic regression had an AUROC of 0.63 (95% confidence interval, 0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were (1) thrombophilia, (2) prior VTE, (3) antiphospholipid antibodies, (4) factor V Leiden mutation, (5) family history of VTE, (6) planned duration of IVC filter (temporary), (7) unable to maintain therapeutic anticoagulation, (8) malignancy, (9) recent or active bleeding, and (10) age. Model performance remained robust across all subgroups. CONCLUSIONS: We developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential to guide patient selection for filter placement, counselling, perioperative management, and follow-up to mitigate filter-related complications and improve outcomes.

Bridging the gap between in vitro and in vivo models: a way forward to clinical translation of mitochondrial transplantation in acute disease states.

Mitochondrial transplantation and transfer are being explored as therapeutic options in acute and chronic diseases to restore cellular function in injured tissues. To limit potential immune responses and rejection of donor mitochondria, current clinical applications have focused on delivery of autologous mitochondria. We recently convened a Mitochondrial Transplant Convergent Working Group (CWG), to explore three key issues that limit clinical translation: (1) storage of mitochondria, (2) biomaterials to enhance mitochondrial uptake, and (3) dynamic models to mimic the complex recipient tissue environment. In this review, we present a summary of CWG conclusions related to these three issues and provide an overview of pre-clinical studies aimed at building a more robust toolkit for translational trials.

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Electrical impedance tomography-guided positive end-expiratory pressure titration in ARDS: a systematic review and meta-analysis.

PURPOSE: Assessing efficacy of electrical impedance tomography (EIT) in optimizing positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients to enhance respiratory system mechanics and prevent ventilator-induced lung injury (VILI), compared to traditional methods. METHODS: We carried out a systematic review and meta-analysis, spanning literature from January 2012 to May 2023, sourced from Scopus, PubMed, MEDLINE (Ovid), Cochrane, and LILACS, evaluated EIT-guided PEEP strategies in ARDS versus conventional methods. Thirteen studies (3 randomized, 10 non-randomized) involving 623 ARDS patients were analyzed using random-effects models for primary outcomes (respiratory mechanics and mechanical power) and secondary outcomes (PaO2/FiO2 ratio, mortality, stays in intensive care unit (ICU), ventilator-free days). RESULTS: EIT-guided PEEP significantly improved lung compliance (n = 941 cases, mean difference (MD) = 4.33, 95% confidence interval (CI) [2.94, 5.71]), reduced mechanical power (n = 148, MD = - 1.99, 95% CI [- 3.51, - 0.47]), and lowered driving pressure (n = 903, MD = - 1.20, 95% CI [- 2.33, - 0.07]) compared to traditional methods. Sensitivity analysis showed consistent positive effect of EIT-guided PEEP on lung compliance in randomized clinical trials vs. non-randomized studies pooled (MD) = 2.43 (95% CI - 0.39 to 5.26), indicating a trend towards improvement. A reduction in mortality rate (259 patients, relative risk (RR) = 0.64, 95% CI [0.45, 0.91]) was associated with modest improvements in compliance and driving pressure in three studies. CONCLUSIONS: EIT facilitates real-time, individualized PEEP adjustments, improving respiratory system mechanics. Integration of EIT as a guiding tool in mechanical ventilation holds potential benefits in preventing ventilator-induced lung injury. Larger-scale studies are essential to validate and optimize EIT's clinical utility in ARDS management.

GLP-1 receptor agonists and atherosclerosis protection: the vascular endothelium takes center stage.

Atherosclerotic cardiovascular disease is a chronic condition that often copresents with type 2 diabetes and obesity. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are incretin mimetics endorsed by major professional societies for improving glycemic status and reducing atherosclerotic risk in people living with type 2 diabetes. Although the cardioprotective efficacy of GLP-1RAs and their relationship with traditional risk factors are well established, there is a paucity of publications that have summarized the potentially direct mechanisms through which GLP-1RAs mitigate atherosclerosis. This review aims to narrow this gap by providing comprehensive and in-depth mechanistic insight into the antiatherosclerotic properties of GLP-1RAs demonstrated across large outcome trials. Herein, we describe the landmark cardiovascular outcome trials that triggered widespread excitement around GLP-1RAs as a modern class of cardioprotective agents, followed by a summary of the origins of GLP-1RAs and their mechanisms of action. The effects of GLP-1RAs at each major pathophysiological milestone of atherosclerosis, as observed across clinical trials, animal models, and cell culture studies, are described in detail. Specifically, this review provides recent preclinical and clinical evidence that suggest GLP-1RAs preserve vessel health in part by preventing endothelial dysfunction, achieved primarily through the promotion of angiogenesis and inhibition of oxidative stress. These protective effects are in addition to the broad range of atherosclerotic processes GLP-1RAs target downstream of endothelial dysfunction, which include systemic inflammation, monocyte recruitment, proinflammatory macrophage and foam cell formation, vascular smooth muscle cell proliferation, and plaque development.

Icosapent ethyl modulates circulating vascular regenerative cell content: The IPE-PREVENTION CardioLink-14 trial.

BACKGROUND: REDUCE-IT (Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial) showed that icosapent ethyl (IPE) reduced major adverse cardiovascular events by 25%. Since the underlying mechanisms for these benefits are not fully understood, the IPE-PREVENTION CardioLink-14 trial (ClinicalTrials.gov: NCT04562467) sought to determine if IPE regulates vascular regenerative (VR) cell content in people with mild to moderate hypertriglyceridemia. METHODS: Seventy statin-treated individuals with triglycerides ≥1.50 and <5.6 mmol/L and either atherosclerotic cardiovascular disease or type 2 diabetes with additional cardiovascular risk factors were randomized to IPE (4 g/day) or usual care. VR cells with high aldehyde dehydrogenase activity (ALDHhi) were isolated from blood collected at the baseline and 3-month visits and characterized with lineage-specific cell surface markers. The primary endpoint was the change in frequency of pro-vascular ALDHhiside scatter (SSC)lowCD133+ progenitor cells. Change in frequencies of ALDHhiSSCmid monocyte and ALDHhiSSChi granulocyte precursor subsets, reactive oxygen species production, serum biomarkers, and omega-3 levels were also evaluated. FINDINGS: Baseline characteristics, cardiovascular risk factors, and medications were balanced between the groups. Compared to usual care, IPE increased the mean frequency of ALDHhiSSClowCD133+ cells (-1.00% ± 2.45% vs. +7.79% ± 1.70%; p = 0.02), despite decreasing overall ALDHhiSSClow cell frequency. IPE assignment also reduced oxidative stress in ALDHhiSSClow progenitors and increased ALDHhiSSChi granulocyte precursor cell content. CONCLUSIONS: IPE-PREVENTION CardioLink-14 provides the first translational evidence that IPE can modulate VR cell content and suggests a novel mechanism that may underlie the cardioprotective effects observed with IPE in REDUCE-IT. FUNDING: HLS Therapeutics provided the IPE in kind and had no role in the study design, conduct, analyses, or interpretation.

Vascular Regenerative Cell Deficiencies in South Asian Adults.

BACKGROUND: South Asian individuals shoulder a disproportionate burden of cardiometabolic diseases. OBJECTIVES: The purpose of this study was to determine if vascular regenerative cell content varies significantly between South Asian and White European people. METHODS: Between January 2022 and January 2023, 60 South Asian and 60 White European adults with either documented cardiovascular disease or established diabetes with ≥1 other cardiovascular risk factor were prospectively enrolled. Vascular regenerative cell content in venous blood was enumerated using a flow cytometry assay that is based on high aldehyde dehydrogenase (ALDHhi) activity and cell surface marker phenotyping. The primary outcome was the difference in frequency of circulating ALDHhi progenitor cells, monocytes, and granulocytes between the 2 groups. RESULTS: Compared with White European participants, those of South Asian ethnicity were younger (69 ± 10 years vs 66 ± 9 years; P < 0.05), had lower weight (88 ± 19 kg vs 75 ± 13 kg; P < 0.001), and exhibited a greater prevalence of type 2 diabetes (62% vs 92%). South Asian individuals had markedly lower circulating frequencies of pro-angiogenic ALDHhiSSClowCD133+ progenitor cells (P < 0.001) and ALDHhiSSCmidCD14+CD163+ monocytes with vessel-reparative capacity (P < 0.001), as well as proportionally more ALDHhi progenitor cells with high reactive oxygen species content (P < 0.05). After correction for sex, age, body mass index, and glycated hemoglobin, South Asian ethnicity was independently associated with lower ALDHhiSSClowCD133+ cell count. CONCLUSIONS: South Asian people with cardiometabolic disease had less vascular regenerative and reparative cells suggesting compromised vessel repair capabilities that may contribute to the excess vascular risk in this population. (The Role of South Asian vs European Origins on Circulating Regenerative Cell Exhaustion [ORIGINS-RCE]; NCT05253521).

Restoration of blood vessel regeneration in the era of combination SGLT2i and GLP-1RA therapy for diabetes and obesity.

Ischaemic cardiovascular diseases, including peripheral and coronary artery disease, myocardial infarction, and stroke, remain major comorbidities for individuals with type 2 diabetes (T2D) and obesity. During cardiometabolic chronic disease (CMCD), hyperglycaemia and excess adiposity elevate oxidative stress and promote endothelial damage, alongside an imbalance in circulating pro-vascular progenitor cells that mediate vascular repair. Individuals with CMCD demonstrate pro-vascular 'regenerative cell exhaustion' (RCE) characterized by excess pro-inflammatory granulocyte precursor mobilization into the circulation, monocyte polarization towards pro-inflammatory vs. anti-inflammatory phenotype, and decreased pro-vascular progenitor cell content, impairing the capacity for vessel repair. Remarkably, targeted treatment with the sodium-glucose cotransporter-2 inhibitor (SGLT2i) empagliflozin in subjects with T2D and coronary artery disease, and gastric bypass surgery in subjects with severe obesity, has been shown to partially reverse these RCE phenotypes. SGLT2is and glucagon-like peptide-1 receptor agonists (GLP-1RAs) have reshaped the management of individuals with T2D and comorbid obesity. In addition to glucose-lowering action, both drug classes have been shown to induce weight loss and reduce mortality and adverse cardiovascular outcomes in landmark clinical trials. Furthermore, both drug families also act to reduce systemic oxidative stress through altered activity of overlapping oxidase and antioxidant pathways, providing a putative mechanism to augment circulating pro-vascular progenitor cell content. As SGLT2i and GLP-1RA combination therapies are emerging as a novel therapeutic opportunity for individuals with poorly controlled hyperglycaemia, potential additive effects in the reduction of oxidative stress may also enhance vascular repair and further reduce the ischaemic cardiovascular comorbidities associated with T2D and obesity.

Using machine learning to predict outcomes following suprainguinal bypass.

OBJECTIVE: Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency. RESULTS: Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses. CONCLUSIONS: We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. RESULTS: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.

Therapeutic Effects of Mesenchymal Stromal Cells Require Mitochondrial Transfer and Quality Control.

Due to their beneficial effects in an array of diseases, Mesenchymal Stromal Cells (MSCs) have been the focus of intense preclinical research and clinical implementation for decades. MSCs have multilineage differentiation capacity, support hematopoiesis, secrete pro-regenerative factors and exert immunoregulatory functions promoting homeostasis and the resolution of injury/inflammation. The main effects of MSCs include modulation of immune cells (macrophages, neutrophils, and lymphocytes), secretion of antimicrobial peptides, and transfer of mitochondria (Mt) to injured cells. These actions can be enhanced by priming (i.e., licensing) MSCs prior to exposure to deleterious microenvironments. Preclinical evidence suggests that MSCs can exert therapeutic effects in a variety of pathological states, including cardiac, respiratory, hepatic, renal, and neurological diseases. One of the key emerging beneficial actions of MSCs is the improvement of mitochondrial functions in the injured tissues by enhancing mitochondrial quality control (MQC). Recent advances in the understanding of cellular MQC, including mitochondrial biogenesis, mitophagy, fission, and fusion, helped uncover how MSCs enhance these processes. Specifically, MSCs have been suggested to regulate peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC1α)-dependent biogenesis, Parkin-dependent mitophagy, and Mitofusins (Mfn1/2) or Dynamin Related Protein-1 (Drp1)-mediated fission/fusion. In addition, previous studies also verified mitochondrial transfer from MSCs through tunneling nanotubes and via microvesicular transport. Combined, these effects improve mitochondrial functions, thereby contributing to the resolution of injury and inflammation. Thus, uncovering how MSCs affect MQC opens new therapeutic avenues for organ injury, and the transplantation of MSC-derived mitochondria to injured tissues might represent an attractive new therapeutic approach.

Empagliflozin improves circulating vascular regenerative cell content in people without diabetes with risk factors for adverse cardiac remodeling.

Sodium glucose-cotransporter 2 (SGLT2) inhibitors have been reported to reduce cardiovascular events and heart failure in people with and without diabetes. These medications have been shown to counter regenerative cell exhaustion in the context of prevalent diabetes. This study sought to determine if empagliflozin attenuates regenerative cell exhaustion in people without diabetes. Peripheral blood mononuclear cells were collected at the baseline and 6-mo visits from individuals randomized to receive empagliflozin (10 mg/day) or placebo who were participating in the EMPA-HEART 2 CardioLink-7 trial. Precursor cell phenotypes were characterized by flow cytometry for cell-surface markers combined with high aldehyde dehydrogenase activity to identify precursor cell subsets with progenitor (ALDHhi) versus mature effector (ALDHlow) cell attributes. Samples from individuals assigned to empagliflozin (n = 25) and placebo (n = 21) were analyzed. At baseline, overall frequencies of primitive progenitor cells (ALDHhiSSClow), monocyte (ALDHhiSSCmid), and granulocyte (ALDHhiSSChi) precursor cells in both groups were similar. At 6 mo, participants randomized to empagliflozin demonstrated increased ALDHhiSSClowCD133+CD34+ proangiogenic cells (P = 0.048), elevated ALDHhiSSCmidCD163+ regenerative monocyte precursors (P = 0.012), and decreased ALDHhiSSCmidCD86 + CD163- proinflammatory monocyte (P = 0.011) polarization compared with placebo. Empagliflozin promoted the recovery of multiple circulating provascular cell subsets in people without diabetes suggesting that the cardiovascular benefits of SGLT2 inhibitors may be attributed in part to the attenuation of vascular regenerative cell exhaustion that is independent of diabetes status.NEW & NOTEWORTHY Using an aldehyde dehydrogenase (ALDH) activity-based flow cytometry assay, we found that empagliflozin treatment for 6 mo was associated with parallel increases in circulating vascular regenerative ALDHhi-CD34/CD133-coexpressing progenitors and decreased proinflammatory ALDHhi-CD14/CD86-coexpressing monocyte precursors in individuals without diabetes but with cardiovascular risk factors. The rejuvenation of the vascular regenerative cell reservoir may represent a mechanism via which sodium glucose-cotransporter 2 (SGLT2) inhibitors limit maladaptive repair and delay the development and progression of cardiovascular diseases.

Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair.

BACKGROUND: Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) carries important perioperative risks; however, there are no widely used outcome prediction tools. The aim of this study was to apply machine learning (ML) to develop automated algorithms that predict 1-year mortality following EVAR. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent elective EVAR for infrarenal AAA between 2003 and 2023. Input features included 47 preoperative demographic/clinical variables. The primary outcome was 1-year all-cause mortality. Data were split into training (70 per cent) and test (30 per cent) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features with logistic regression as the baseline comparator. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. RESULTS: Some 63 655 patients were included. One-year mortality occurred in 3122 (4.9 per cent) patients. The best performing prediction model for 1-year mortality was XGBoost, achieving an AUROC (95 per cent c.i.) of 0.96 (0.95-0.97). Comparatively, logistic regression had an AUROC (95 per cent c.i.) of 0.69 (0.68-0.71). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.04. The top 3 predictive features in the algorithm were 1) unfit for open AAA repair, 2) functional status, and 3) preoperative dialysis. CONCLUSIONS: In this data set, machine learning was able to predict 1-year mortality following EVAR using preoperative data and outperformed standard logistic regression models.

Inhalation of ACE2 as a therapeutic target on sex-bias differences in SARS-CoV-2 infection and variant of concern.

Despite similar infection rates, COVID-19 has resulted in more deaths in men than women. To understand the underlying mechanisms behind this sex-biased difference in disease severity, we infected K18-human angiotensin converting enzyme 2 (ACE2) mice of both sexes with SARS-CoV-2. Our study revealed a unique protein expression profile in the lung microenvironment of female mice. As a result, they were less vulnerable to severe infection, with higher ACE2 expression and a higher estrogen receptor α (ERα)/androgen receptor (AR) ratio that led to increased antiviral factor levels. In male mice, inhaling recombinant ACE2 neutralized the virus and maintained the ERα/AR ratio, thereby protecting the lungs. Our findings suggest that inhaling recombinant ACE2 could serve as a decoy receptor against SARS-CoV-2 and protect male mice by offsetting ERα-associated protective mechanisms. Additionally, our study supports the potential effectiveness of recombinant ACE2 therapy in human lung organoids infected with the Delta variant.

Using machine learning to predict outcomes following open abdominal aortic aneurysm repair.

OBJECTIVE: Prediction of outcomes following open abdominal aortic aneurysm (AAA) repair remains challenging with a lack of widely used tools to guide perioperative management. We developed machine learning (ML) algorithms that predict outcomes following open AAA repair. METHODS: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent elective open AAA repair between 2003 and 2023. Input features included 52 preoperative demographic/clinical variables. All available preoperative variables from VQI were used to maximize predictive performance. The primary outcome was in-hospital major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death). Secondary outcomes were individual components of the primary outcome, other in-hospital complications, and 1-year mortality and any reintervention. We split our data into training (70%) and test (30%) sets. Using 10-fold cross-validation, six ML models were trained using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. The top 10 predictive features in our final model were determined based on variable importance scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median area deprivation index, proximal clamp site, prior aortic surgery, and concomitant procedures. RESULTS: Overall, 12,027 patients were included. The primary outcome of in-hospital MACE occurred in 630 patients (5.2%). Compared with patients without a primary outcome, those who developed in-hospital MACE were older with more comorbidities, demonstrated poorer functional status, had more complex aneurysms, and were more likely to require concomitant procedures. Our best performing prediction model for in-hospital MACE was XGBoost, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). Comparatively, logistic regression had an AUROC of 0.71 (95% confidence interval, 0.70-0.73). For secondary outcomes, XGBoost achieved AUROCs between 0.84 and 0.94. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. These findings highlight the excellent predictive performance of the XGBoost model. The top three predictive features in our algorithm for in-hospital MACE following open AAA repair were: (1) coronary artery disease; (2) American Society of Anesthesiologists classification; and (3) proximal clamp site. Model performance remained robust on all subgroup analyses. CONCLUSIONS: Open AAA repair outcomes can be accurately predicted using preoperative data with our ML models, which perform better than logistic regression. Our automated algorithms can help guide risk-mitigation strategies for patients being considered for open AAA repair to improve outcomes.

SARS-CoV-2 RBD and Its Variants Can Induce Platelet Activation and Clearance: Implications for Antibody Therapy and Vaccinations against COVID-19.

The COVID-19 pandemic caused by SARS-CoV-2 virus is an ongoing global health burden. Severe cases of COVID-19 and the rare cases of COVID-19 vaccine-induced-thrombotic-thrombocytopenia (VITT) are both associated with thrombosis and thrombocytopenia; however, the underlying mechanisms remain inadequately understood. Both infection and vaccination utilize the spike protein receptor-binding domain (RBD) of SARS-CoV-2. We found that intravenous injection of recombinant RBD caused significant platelet clearance in mice. Further investigation revealed the RBD could bind platelets, cause platelet activation, and potentiate platelet aggregation, which was exacerbated in the Delta and Kappa variants. The RBD-platelet interaction was partially dependent on the ß3 integrin as binding was significantly reduced in ß3-/- mice. Furthermore, RBD binding to human and mouse platelets was significantly reduced with related αIIbß3 antagonists and mutation of the RGD (arginine-glycine-aspartate) integrin binding motif to RGE (arginine-glycine-glutamate). We developed anti-RBD polyclonal and several monoclonal antibodies (mAbs) and identified 4F2 and 4H12 for their potent dual inhibition of RBD-induced platelet activation, aggregation, and clearance in vivo, and SARS-CoV-2 infection and replication in Vero E6 cells. Our data show that the RBD can bind platelets partially though αIIbß3 and induce platelet activation and clearance, which may contribute to thrombosis and thrombocytopenia observed in COVID-19 and VITT. Our newly developed mAbs 4F2 and 4H12 have potential not only for diagnosis of SARS-CoV-2 virus antigen but also importantly for therapy against COVID-19.

Augmented Parkin-dependent mitophagy underlies the hepatoprotective effect of remote ischemic conditioning used prior to hemorrhagic shock.

BACKGROUND AND AIMS: Hemorrhagic shock-resuscitation (HSR) following trauma contributes to organ dysfunction by causing ischemia-reperfusion injury (IRI). We previously showed that 'remote ischemic preconditioning' (RIPC) exerted multi-organ protection from IRI. Maintenance of mitochondrial quality by clearance of dysfunctional mitochondria via mitophagy is vital in restoring organ integrity. We hypothesized that parkin-dependent mitophagy played a role in RIPC-induced hepatoprotection following HSR. METHODS: The hepatoprotective effect of RIPC in a murine model of HSR-IRI was investigated in wild type and parkin-/- animals. Mice were subjected to HSR ± RIPC and blood and organs were collected, followed by cytokine ELISAs, histology, qPCR, Western blots, and transmission electron microscopy. RESULTS: HSR increased hepatocellular injury, as measured by plasma ALT and liver necrosis, while antecedent RIPC prevented this injury; in parkin-/- mice, RIPC failed to exert hepatoprotection. The ability of RIPC to lessen HSR-induced rises in plasma IL-6 and TNFα, was lost in parkin-/- mice. While RIPC alone did not induce mitophagy, the application of RIPC prior to HSR caused a synergistic increase in mitophagy, this increase was not observed in parkin-/- mice. RIPC induced shifts in mitochondrial morphology favoring mitophagy in WT but not in parkin-/- animals. CONCLUSIONS: RIPC was hepatoprotective in WT mice following HSR but not in parkin-/- mice. Loss of protection in parkin-/- mice corresponded with the failure of RIPC plus HSR to upregulate the mitophagic process. Improving mitochondrial quality by modulating mitophagy, may prove to be an attractive therapeutic target in disease processes caused by IRI.

Key Role of Mesenchymal Stromal Cell Interaction with Macrophages in Promoting Repair of Lung Injury.

Lung macrophages (Mφs) are essential for pulmonary innate immunity and host defense due to their dynamic polarization and phenotype shifts. Mesenchymal stromal cells (MSCs) have secretory, immunomodulatory, and tissue-reparative properties and have shown promise in acute and chronic inflammatory lung diseases and in COVID-19. Many beneficial effects of MSCs are mediated through their interaction with resident alveolar and pulmonary interstitial Mφs. Bidirectional MSC-Mφ communication is achieved through direct contact, soluble factor secretion/activation, and organelle transfer. The lung microenvironment facilitates MSC secretion of factors that result in Mφ polarization towards an immunosuppressive M2-like phenotype for the restoration of tissue homeostasis. M2-like Mφ in turn can affect the MSC immune regulatory function in MSC engraftment and tissue reparatory effects. This review article highlights the mechanisms of crosstalk between MSCs and Mφs and the potential role of their interaction in lung repair in inflammatory lung diseases.