Strain-Sensor-In-Pixel Technology for Resolution-Sustainable Stretchable Displays.

One of the limitations of stretchable displays is the severe degradation of resolution or the decrease in the number of pixels per unit area when stretched. Hence, we suggest a strain-sensor-in-pixel (S-SIP) system through the adoption of hidden pixels that are activated only during the stretch mode for maintaining the density of on-state pixels. For the S-SIP system, the gate and source electrodes of InGaZnO thin-film transistors (TFTs) in an existing pixel are connected to a resistive strain sensor through the facile and selective deposition of silver nanowires (AgNWs) via electrohydrodynamic-jet-printing. With this approach, the strain sensor integrated TFT functions as a strain-triggered switch, which responds only to stretching along the designated axes by finely tuning the orientation and cycles of AgNW printing. The strain sensor-integrated TFT remains in an off-state when unstretched and switches to an on-state when stretched, exhibiting a large negative gauge factor of -1.1 × 1010 and a superior mechanical stability enduring 6000 cycles, which enables the efficient structure to operate hidden pixels without requiring additional signal processing. Furthermore, the stable operation of the S-SIP in a 5 × 5-pixel array is demonstrated via circuit simulation, implying the outstanding applicability and process compatibility to the conventional active-matrix display backplanes.

Long-Term Prognostic Value of Infarct Transmurality Determined by Contrast-Enhanced Cardiac Magnetic Resonance after ST-Segment Elevation Myocardial Infarction.

The long-term prognostic significance of maximal infarct transmurality evaluated by contrast-enhanced cardiac magnetic resonance (CE-CMR) in ST-segment elevation myocardial infarction (STEMI) patients has yet to be determined. This study aimed to see if maximal infarct transmurality has any additional long-term prognostic value over other CE-CMR predictors in STEMI patients, such as microvascular obstruction (MVO) and intramyocardial hemorrhage (IMH). The study included 112 consecutive patients who underwent CE-CMR after STEMI to assess established parameters of myocardial injury as well as the maximal infarct transmurality. The primary clinical endpoint was the occurrence of major adverse cardiac events (MACE), which included all-cause death, non-fatal reinfarction, and new heart failure hospitalization. The MACE occurred in 10 patients over a median follow-up of 7.9 years (IQR, 5.8 to 9.2 years) (2 deaths, 3 nonfatal MI, and 5 heart failure hospitalization). Patients with MACE had significantly higher rates of transmural extent of infarction, infarct size >5.4 percent, MVO, and IMH compared to patients without MACE. In stepwise multivariable Cox regression analysis, the transmural extent of infarction defined as 75 percent or more of infarct transmurality was an independent predictor of the MACE after correction for MVO and IMH (hazard ratio 8.7, 95% confidence intervals [CIs] 1.1-71; p=0.043). In revascularized STEMI patients, post-infarction CE-CMR-based maximal infarct transmurality is an independent long-term prognosticator. Adding maximal infarct transmurality to CE-CMR parameters like MVO and IMH could thus identify patients at high risk of long-term adverse outcomes in STEMI.

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.

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.

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.

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.

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.

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.