Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling.

BACKGROUND: Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process. METHOD: Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters. RESULTS: Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance. CONCLUSION: Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

Seven-year follow-up of endovascular treatment of iatrogenic brachioradial artery injury complicating percutaneous coronary intervention: a case report.

The radial artery has been used increasingly for percutaneous coronary intervention because of its safety and feasible access route. Nevertheless, transradial complications are possible because of the variation in radial artery anatomy. We experienced a case of the brachioradial artery injury secondary to catheterization, presenting as hypovolemic shock. A 76-year-old woman presented at our emergency department complaining of effort-induced angina. Coronary angiography via the right radial artery showed critical stenosis in the middle of the left anterior descending coronary artery. After wiring into this vessel, balloon angioplasty using a 6-Fr Judkin left guiding catheter was performed with the deployment of the zotarolimus-eluting stent. There was difficulty in negotiating the guidewire and balloons in that resistance was experienced while passing the catheter in the upper arm. Therefore, retrograde radial arteriography was performed to determine any injury to radial artery. This showed contrast extravasation in the brachioradial artery. Initially, manual compression was tried. However, 2 hours later, the patient developed cold sweating and went into a stupor. Laboratory findings showed a decline in hemoglobin, leading to suspicion of hemorrhagic shock. We applied over 30 minutes of balloon inflation, but this was ineffective. While surgical repair was not available, a 6.0 × 50 mm Viabahn stent was placed over the axillary artery. Subsequent angiography showed no further leakage or occlusion of the brachioradial artery. The postprocedural period was uneventful, and the patient was discharged with dual antiplatelet agents. At a 7-year clinical follow-up, the patient was free from limb ischemia symptoms.

Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department.

OBJECTIVE: To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation. METHODS: This retrospective study included adults who visited the ED afebrile at initial presentation. We developed four models based on artificial neural networks to identify suspected serious infection. Patient demographics, vital signs, laboratory test results and information extracted from initial ED physician notes using term frequency-inverse document frequency were used as model variables. Models were trained and internally validated with data from one hospital and externally validated using data from a different hospital. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). RESULTS: The training, internal validation, and external validation datasets comprised 150,699, 37,675, and 85,098 patients, respectively. The AUCs (95% CIs) for Models 1 (demographics + vital signs), 2 (demographics + vital signs + initial ED physician note), 3 (demographics + vital signs + laboratory tests), and 4 (demographics + vital signs + laboratory tests + initial ED physician note) in the internal validation dataset were 0.789 (0.782-0.796), 0.867 (0.862-0.872), 0.881 (0.876-0.887), and 0.911 (0.906-0.915), respectively. In the external validation dataset, the AUCs (95% CIs) of Models 1, 2, 3, and 4 were 0.824 (0.817-0.830), 0.895 (0.890-0.899), 0.879 (0.873-0.884), and 0.913 (0.909-0.917), respectively. Model 1 can be utilized immediately after ED triage, Model 2 can be utilized after the initial physician notes are recorded (median time from ED triage: 28 min), and Models 3 and 4 can be utilized after the initial laboratory tests are reported (median time from ED triage: 68 min). CONCLUSIONS: We developed and validated models to identify suspected serious infection in the ED. Extracted information from initial ED physician notes using NLP contributed to increased model performance, permitting identification of suspected serious infection at early stages of ED visits.

Effect of first epinephrine administration time on cerebral perfusion pressure and cortical cerebral blood flow in a porcine cardiac arrest model.

OBJECTIVE: The optimal time for epinephrine administration and its effects on cerebral blood flow (CBF) and microcirculation remain controversial. This study aimed to assess the effect of the first administration of epinephrine on cerebral perfusion pressure (CePP) and cortical CBF in porcine cardiac arrest model. METHODS: After 4 min of untreated ventricular fibrillation, eight of 24 swine were randomly assigned to the early, intermediate, and late groups. In each group, epinephrine was administered intravenously at 5, 10, and 15 min after cardiac arrest induction. CePP was calculated as the difference between the mean arterial pressure and intracranial pressure. Cortical CBF was measured using a laser Doppler flow probe. The outcomes were CePP and cortical CBF measured continuously during cardiopulmonary resuscitation (CPR). Mean CePP and cortical CBF were compared using analysis of variance and a linear mixed model. RESULTS: The mean CePP was significantly different between the groups at 6-11 min after cardiac arrest induction. The mean CePP in the early group was significantly higher than that in the intermediate group at 8-10 min and that in the late group at 6-9 min and 10-11 min. The mean cortical CBF was significantly different between the groups at 9-11 min. The mean cortical CBF was significantly higher in the early group than in the intermediate and late group at 9-10 min. CONCLUSION: Early administration of epinephrine was associated with improved CePP and cortical CBF compared to intermediate or late administration during the early period of CPR.

Pacing-induced cardiomyopathy in patients with preserved ejection fraction undergoing permanent cardiac pacemaker placement.

BACKGROUND: Chronic right-ventricular (RV) pacing can worsen heart failure in patients with a low ejection fraction (EF), but little is known about pacing-induced cardiomyopathy (PICM) in patients with preserved EF. We aimed to investigate risk factors of PICM in these patients during long-term follow-up. METHODS: The prospective registry at Chosun University Hospital, South Korea, included de novo patients with preserved EF undergoing transvenous permanent pacemaker (PPM) implantation for atrioventricular blockage from 2017 to 2021. Patients with EF ≥ 50% and expected ventricular pacing ≥ 40% were included. Composite outcomes were cardiac death (pump failure), hospitalization because of heart failure, PICM, and biventricular pacing (BVP) upgrade. RESULTS: A total of 168 patients (69 men, 76.3 ± 10.4 years) were included. During three years of follow-up, one patient died, 14 were hospitalized, 16 suffered PICM, and two underwent BVP upgrade. PICM were associated with reduced global longitudinal strain (GLS), prolonged paced QRS duration (pQRSd) and diastolic variables (E/e', LAVI). Cox regression analysis identified pQRSd (hazard ratio [HR], 1.111; 95% confidence interval [CI], 1.011-1.222; P = 0.03) and reduced GLS (HR, 1.569; 95% CI, 1.163-2.118; P = 0.003) as independent predictors of PICM. GLS showed high predictive accuracy for PICM, with an area under the curve of 0.84 (95% CI 0.779-0.894; P < 0.001) [GLS -12.0, 62.5% sensitivity, and 86.1% specificity]. CONCLUSION: RV pacing increased the risk of PICM in patients with preserved EF. Reduced GLS and prolonged pQRSd could help identify individuals at high risk of PICM even with preserved EF.

Measurement of level of consciousness by AVPU scale assessment system based on automated video and speech recognition technology.

OBJECTIVE: To develop an alert/verbal/painful/unresponsive (AVPU) scale assessment system based on automated video and speech recognition technology (AVPU-AVSR) that can automatically assess a patient's level of consciousness and evaluate its performance through clinical simulation. METHODS: We developed an AVPU-AVSR system with a whole-body camera, face camera, and microphone. The AVPU-AVSR system automatically extracted essential audiovisual features to assess the AVPU score from the recorded video files. Arm movement, pain stimulus, and eyes-open state were extracted using a rule-based approach using landmarks estimated from pre-trained pose and face estimation models. Verbal stimuli were extracted using a pre-trained speech-recognition model. Simulations of a physician examining the consciousness of 12 simulated patients for 16 simulation scenarios (4 for each of "Alert", "Verbal", "Painful", and "Unresponsive") were conducted under the AVPU-AVSR system. The accuracy, sensitivity, and specificity of the AVPU-AVSR system were assessed. RESULTS: A total of 192 cases with 12 simulated patients were assessed using the AVPU-AVSR system with a multi-class accuracy of 0.95 (95% confidence interval [CI] (0.92-0.98). The sensitivity and specificity (95% CIs) for detecting impaired consciousness were 1.00 (0.97-1.00) and 0.88 (0.75-0.95), respectively. The sensitivity and specificity of each extracted feature ranged from 0.88 to 1.00 and 0.98 to 1.00. CONCLUSIONS: The AVPU-AVSR system showed good accuracy in assessing consciousness levels in a clinical simulation and has the potential to be implemented in clinical practice to automatically assess mental status.

Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty.

Prediction of bacteremia is a clinically important but challenging task. An artificial intelligence (AI) model has the potential to facilitate early bacteremia prediction, aiding emergency department (ED) physicians in making timely decisions and reducing unnecessary medical costs. In this study, we developed and externally validated a Bayesian neural network-based AI bacteremia prediction model (AI-BPM). We also evaluated its impact on physician predictive performance considering both AI and physician uncertainties using historical patient data. A retrospective cohort of 15,362 adult patients with blood cultures performed in the ED was used to develop the AI-BPM. The AI-BPM used structured and unstructured text data acquired during the early stage of ED visit, and provided both the point estimate and 95% confidence interval (CI) of its predictions. High AI-BPM uncertainty was defined as when the predetermined bacteremia risk threshold (5%) was included in the 95% CI of the AI-BPM prediction, and low AI-BPM uncertainty was when it was not included. In the temporal validation dataset (N = 8,188), the AI-BPM achieved area under the receiver operating characteristic curve (AUC) of 0.754 (95% CI 0.737-0.771), sensitivity of 0.917 (95% CI 0.897-0.934), and specificity of 0.340 (95% CI 0.330-0.351). In the external validation dataset (N = 7,029), the AI-BPM's AUC was 0.738 (95% CI 0.722-0.755), sensitivity was 0.927 (95% CI 0.909-0.942), and specificity was 0.319 (95% CI 0.307-0.330). The AUC of the post-AI physicians predictions (0.703, 95% CI 0.654-0.753) was significantly improved compared with that of the pre-AI predictions (0.639, 95% CI 0.585-0.693; p-value < 0.001) in the sampled dataset (N = 1,000). The AI-BPM especially improved the predictive performance of physicians in cases with high physician uncertainty (low subjective confidence) and low AI-BPM uncertainty. Our results suggest that the uncertainty of both the AI model and physicians should be considered for successful AI model implementation.

ETV2 Enhances CXCL5 Secretion from Endothelial Cells, Leading to the Promotion of Vascular Smooth Muscle Cell Migration.

Abnormal communication between endothelial cells (ECs) and vascular smooth muscle cells (VSMCs) promotes vascular diseases, including atherogenesis. ETS variant transcription factor 2 (ETV2) plays a substantial role in pathological angiogenesis and the reprogramming of ECs; however, the role of ETV2 in the communication between ECs and VSMCs has not been revealed. To investigate the interactive role of ETV2 in the EC to VSMC phenotype, we first showed that treatment with a conditioned medium from ETV2-overexpressed ECs (Ad-ETV2 CM) significantly increased VSMC migration. The cytokine array showed altered levels of several cytokines in Ad-ETV2 CM compared with those in normal CM. We found that C-X-C motif chemokine 5 (CXCL5) promoted VSMC migration using the Boyden chamber and wound healing assays. In addition, an inhibitor of C-X-C motif chemokine receptor 2 (CXCR2) (the receptor for CXCL5) significantly inhibited this process. Gelatin zymography showed that the activities of matrix metalloproteinase (MMP)-2 and MMP-9 increased in the media of VSMCs treated with Ad-ETV2 CM. Western blotting revealed a positive correlation between Akt/p38/c-Jun phosphorylation and CXCL5 concentration. The inhibition of Akt and p38-c-Jun effectively blocked CXCL5-induced VSMC migration. In conclusion, CXCL5 from ECs induced by ETV2 promotes VSMC migration via MMP upregulation and the activation of Akt and p38/c-Jun.

Evaluation of Socioeconomic Position and Survival After Out-of-Hospital Cardiac Arrest in Korea Using Structural Equation Modeling.

Importance: The association between low socioeconomic position (SEP) and poor survival after out-of-hospital cardiac arrest (OHCA) has not been thoroughly investigated. Objectives: To evaluate the association between individual SEP and survival after OHCA and to identify any mediating pathways using structural equation modeling (SEM). Design, Setting, and Participants: This is a retrospective cohort study that used data collected from January 2013 to December 2019. Participants were adults with OHCA with a presumed cardiac etiology. The study was conducted in Korea, which has a universal health insurance system. Data were analyzed from January 2022 to February 2023. Exposures: Individual SEP was measured by insurance type (National Health Insurance [NHI] and medical aid [MA]) and premiums. SEP was categorized into 5 groups, in which NHI beneficiaries were divided into quartiles (Q1, highest quartile; Q4, lowest quartile), and MA was the lowest SEP group. Main Outcomes and Measures: The primary outcome was survival to discharge. The association between SEP and OHCA survival was examined using multivariable logistic regression, and mediating factors were identified using SEM. Results: A total of 121 516 patients (median [IQR] age, 73 [60-81] years; 43 912 [36.1%] female patients) were included. Compared with the NHI Q1 group, individuals with lower SEP had lower odds of survival to discharge. The adjusted odds ratios of survival to discharge were 0.97 (95% CI, 0.94-1.00), 0.88 (95% CI, 0.85-0.91), 0.91 (95% CI, 0.88-0.94), and 0.53 (95% CI, 0.50-0.56) for the NHI Q2, NHI Q3, NHI Q4, and MA groups, respectively. Several factors were found to mediate the association of SEP and survival in the total study population, with mediating proportions of 15.1% (95% CI, 11.8%-18.4%) for witnessed status, 4.8% (95% CI, 3.5%-6.0%) for bystander cardiopulmonary resuscitation provision, 41.8% (95% CI, 35.4%-48.1%) for initial rhythm, and 9.4% (95% CI, 7.4%-11.4%) for emergency department level. Among patients who survived to hospital admission, the mediation proportions were 11.8% (95% CI, 6.7%-16.9%) for witnessed status, 3.7% (95% CI, 1.3%-6.1%) for bystander cardiopulmonary resuscitation provision, 56.2% (95% CI, 41.0%-71.4%) for initial rhythm, 10.7% (95% CI, 6.1%-15.3%) for emergency department level, 20.2% (95% CI, 14.0%-26.5%) for coronary angiography, and 4.2% (95% CI, 2.2%-6.1%) for targeted temperature management. Conclusions and Relevance: In this cohort study of patients with OHCA, lower individual SEP was significantly associated with lower survival to discharge. Potentially modifiable mediators can be targeted for public health interventions to reduce disparities in survival among patients with OHCA of different SEP.

Initial Experience with Left Bundle Branch Area Pacing in Patients with Atrioventricular Block and Impaired LV Function.

Chronic right ventricular (RV) pacing can exacerbate heart failure in patients with a low left ventricular ejection fraction (LVEF). Left bundle branch area pacing (LBBAP) has emerged as a novel physiological pacing technique; however, information remains limited on its use among patients with a low EF. This study investigated the safety and short-term clinical outcomes of LBBAP among patients with impaired left ventricular (LV) function. This retrospective analysis of pacemakers at Chosun University Hospital, South Korea, included all patients with impaired LV function (EF<50%) who underwent pacemaker implantation for atrioventricular blockage from 2019-2022. Clinical characteristics, 12-lead electrocardiography findings, echocardiography findings, and laboratory parameters were evaluated. Composite outcomes were defined as all-cause mortality, cardiac death, and hospitalization due to heart failure during the 6-month follow-up. Altogether 57 patients (25 men; mean age, 77.4±10.8 y; LVEF, 41.5±3.8%) were divided into LBBAP (n=16), biventricular pacing (BVP; n=16), and conventional RV pacing (RVP; n=25) groups. In the LBBAP group, the mean paced QRS duration (pQRSd) was narrower (119.5±14.7 vs. 140.2±14.3 vs. 163.2±13.9; p<0.001) and cardiac troponin I level was elevated post-pacing (1.14±1.29 vs. 0.20±0.29 vs. 0.24±0.51, p=0.001). Lead parameters were stable. One patient was hospitalized, and four died (one patient each from heart failure admission, myocardial infarction, unexplained death, and pneumonia in RVP vs. one from intracerebral hemorrhage in BVP) during the follow-up period. In conclusion, LBBAP is feasible in patients with impaired LV function without acute or significant complications and provides a remarkably narrower pQRSd with a stable pacing threshold.

Machine Learning Analysis to Identify Data Entry Errors in Prehospital Patient Care Reports: A Case Study of a National Out-of-Hospital Cardiac Arrest Registry.

Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database.Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable.Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71-0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data.Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models.

Efficient Photon Extraction in Top-Emission Organic Light-Emitting Devices Based on Ampicillin Microstructures.

The desire to enhance the efficiency of organic light-emitting devices (OLEDs) has driven to the investigation of advanced materials with fascinating properties. In this work, the efficiency of top-emission OLEDs (TEOLEDs) is enhanced by introducing ampicillin microstructures (Amp-MSs) with dual phases (α-/ß-phase) that induce photoluminescence (PL) and electroluminescence (EL). Moreover, Amp-MSs can adjust the charge balance by Fermi level (EF ) alignment, thereby decreasing the leakage current. The decrease in the wave-guided modes can enhance the light outcoupling through optical scattering. The resulting TEOLED demonstrates a record-high external quantum efficiency (EQE) (maximum: 68.7% and average: 63.4% at spectroradiometer; maximum: 44.8% and average: 42.6% at integrating sphere) with a wider color gamut (118%) owing to the redshift of the spectrum by J-aggregation. Deconvolution of the EL intensities is performed to clarify the contribution of Amp-MSs to the device EQE enhancement (optical scattering by Amp-MSs: 17.0%, PL by radiative energy transfer: 9.1%, and EL by J-aggregated excitons: 4.6%). The proposed TEOLED outperforms the existing frameworks in terms of device efficiency.

Mechanically Durable Organic/High-k Inorganic Hybrid Gate Dielectrics Enabled by Plasma-Polymerization of PTFE for Flexible Electronics.

To achieve both the synergistic advantages of outstanding flexibility in organic dielectrics and remarkable dielectric/insulating properties in inorganic dielectrics, a plasma-polymerized hafnium oxide (HfOx) hybrid (PPH-hybrid) dielectric is proposed. Using a radio-frequency magnetron cosputtering process, the high-k HfOx dielectric is plasma-polymerized with polytetrafluoroethylene (PTFE), which is a flexible, thermally stable, and hydrophobic fluoropolymer dielectric. The PPH-hybrid dielectric with a high dielectric constant of 14.17 exhibits excellent flexibility, maintaining a leakage current density of ∼10-8 A/cm2 even after repetitive bending stress (up to 10000 bending cycles with a radius of 2 mm), whereas the HfOx dielectric degrades to be leaky. To evaluate its practical applicability to flexible thin-film transistors (TFTs), the PPH-hybrid dielectric is applied to amorphous indium-gallium-zinc oxide (IGZO) TFTs as a gate dielectric. Consequently, the PPH-hybrid dielectric-based IGZO TFTs exhibit stable electrical performance under the same harsh bending cycles: a field-effect mobility of 16.99 cm2/(V s), an on/off current ratio of 1.15 × 108, a subthreshold swing of 0.35 V/dec, and a threshold voltage of 0.96 V (averaged in nine devices). Moreover, the PPH-hybrid dielectric-based IGZO TFTs exhibit a reduced I-V hysteresis and an enhanced positive bias stress stability, with the threshold voltage shift decreasing from 4.99 to 1.74 V, due to fluorine incorporation. These results demonstrate that PTFE improves both the mechanical durability and electrical stability, indicating that the PPH-hybrid dielectric is a promising candidate for high-performance and low-power flexible electronics.

Plantamajoside Attenuates Neointima Formation via Upregulation of Tissue Inhibitor of Metalloproteinases in Balloon-Injured Rats.

The abnormal change of vascular smooth muscle cell (VSMC) behavior is an important cellular event leading to neointimal hyperplasia in atherosclerosis and restenosis. Plantamajoside (PMS), a phenylethanoid glycoside compound of the Plantago asiatica, has been reported to have anti-inflammatory, antioxidative, and anticancer activities. In this study, the protective effects of PMS against intimal hyperplasia and the mechanisms underlying the regulation of VSMC behavior were investigated. MTT and BrdU assays were performed to evaluate the cytotoxicity and cell proliferative activity of PMS, respectively. Rat aortic VSMC migrations after treatment with the determined concentration of PMS (50 and 150 µM) were evaluated using wound healing and Boyden chamber assays. The inhibitory effects of PMS on intimal hyperplasia were evaluated in balloon-injured (BI) rat carotid artery. PMS suppressed the proliferation in platelet-derived growth factor-BB-induced VSMC, as confirmed from the decrease in cyclin-dependent kinase (CDK)-2, CDK-4, cyclin D1, and proliferating cell nuclear antigen levels. PMS also inhibited VSMC migration, consistent with the downregulated expression and zymolytic activities of matrix metalloproteinase (MMP)2, MMP9, and MMP13. PMS specifically regulated MMP expression through p38 mitogen-activated protein kinase and focal adhesion kinase pathways. Tissue inhibitor of metalloproteinase (TIMP)1 and TIMP2 levels were upregulated via Smad1. TIMPs inhibited the conversion of pro-MMPs to active MMPs. PMS significantly inhibited neointimal formation in BI rat carotid arteries. In conclusion, PMS inhibits VSMC proliferation and migration by upregulating TIMP1 and TIMP2 expression. Therefore, PMS could be a potential therapeutic agent for vascular atherosclerosis and restenosis treatment.

Oxidative stress generated by polycyclic aromatic hydrocarbons from ambient particulate matter enhance vascular smooth muscle cell migration through MMP upregulation and actin reorganization.

BACKGROUND: Epidemiological studies have suggested that elevated concentrations of particulate matter (PM) are strongly associated with the incidence of atherosclerosis, however, the underlying cellular and molecular mechanisms of atherosclerosis by PM exposure and the components that are mainly responsible for this adverse effect remain to be established. In this investigation, we evaluated the effects of ambient PM on vascular smooth muscle cell (VSMC) behavior. Furthermore, the effects of polycyclic aromatic hydrocarbons (PAHs), major components of PM, on VSMC migration and the underlying mechanisms were examined. RESULTS: VSMC migration was significantly increased by treatment with organic matters extracted from ambient PM. The total amount of PAHs contained in WPM was higher than that in SPM, leading to higher ROS generation and VSMC migration. The increased migration was successfully inhibited by treatment with the anti-oxidant, N-acetyl-cysteine (NAC). The levels of matrix metalloproteinase (MMP) 2 and 9 were significantly increased in ambient PM-treated VSMCs, with MMP9 levels being significantly higher in WPM-treated VSMCs than in those treated with SPM. As expected, migration was significantly increased in all tested PAHs (anthracene, ANT; benz(a)anthracene, BaA) and their oxygenated derivatives (9,10-Anthraquinone, AQ; 7,12-benz(a)anthraquinone, BAQ, respectively). The phosphorylated levels of focal adhesion kinase (FAK) and formation of the focal adhesion complex were significantly increased in ambient PM or PAH-treated VSMCs, and these effects were blocked by administration of NAC or α-NF, an inhibitor of AhR, the receptor that allows PAH uptake. Subsequently, the levels of phosphorylated Src and NRF, the downstream targets of FAK, were altered with a pattern similar to that of p-FAK. CONCLUSIONS: PAHs, including oxy-PAHs, in ambient PM may have dual effects that lead to an increase in VSMC migration. One is the generation of oxidative stress followed by MMP upregulation, and the other is actin reorganization that results from the activation of the focal adhesion complex.