Factors influencing the prognosis of acute basilar artery occlusion patients treated endovascularly: the impact of treatment time window and preoperative symptoms.

Objective: The aim of this study was to examine the factors influencing the prognosis of patients diagnosed with acute basilar artery occlusion (BAO) who receive endovascular treatment. Our particular emphasis was on the predictive implications of the time window for treatment (from symptom onset to femoral artery puncture) and preoperative symptoms for prognosis. Methods: A retrospective analysis of data collected from 51 BAO patients who received endovascular treatment at the Neurosurgery Department of Jinhua Central Hospital from April 2018 to October 2021 was undertaken. The data included immediate post-interventional recanalization rates and the 90-day clinical prognoses of the patients. We used the Modified Rankin Scale (mRs) to categorize patients into two prognosis groups: a favorable prognosis group (mRs score ≤2) and an unfavorable prognosis group (mRs score >2). Preoperative symptoms were gauged using the Glasgow Coma Scale (GCS) and National Institutes of Health Stroke Scale (NIHSS) scores. A logistic regression analysis was conducted to identify risk factors affecting the prognosis of BAO patients following endovascular treatment. Results: The procedure resulted in complete recanalization in all patients (100%). However, four patients (7.8%) passed away during the postoperative hospitalization period. The remaining 47 patients were followed up for 3 months. It was found that 15 patients (31.91%) had a favorable prognosis, while 32 (68.09%) had an unfavorable prognosis. It was generally observed that patients with an unfavorable prognosis had notably higher preoperative GCS and NIHSS scores (p < 0.05). Logistic regression analysis revealed that preoperative symptom severity, as indicated by NIHSS score, and treatment time window were significant prognostic risk factors for patients undergoing endovascular treatment for BAO (p < 0.05). Conclusion: Endovascular intervention for BAO appears to be safe and effective, with greater likelihood of a favorable prognosis in patients treated within ≤6 h. The chances of favorable prognosis could potentially be linked to the severity of the patient's preoperative symptoms.

A Real-Time Respiration Monitoring System Using WiFi Sensing Based on the Concentric Circle Model.

This paper proposes and experimentally validates a novel concentric circle (CC) model for indoor WiFi sensing. By setting the transmitter and receiver together, the perception model becomes concentric circles with equal spacing, eliminating the blind zone and unequal radial sensitivity problems of the Fresnel zone (FZ) model. Then a human respiratory monitoring system is developed based on this model, which executes the following steps: (1) Principal component analysis (PCA) is applied to the channel state information ratio (CSIR) as a preprocessing to extract the components related to human activities. (2) Human presence and respiratory signal detection are adopted to improve monitoring accuracy. (3) The Doppler respiratory frequency is extracted to calculate the respiratory rate. Experimental results show that the CC model achieves high accuracy in velocity measurement with an error of less than 0.4 cm/s. The respiration monitoring system can accurately monitor human respiration with an error of less than 0.7 bpm within 6 m.

Regularized robust estimation in binary regression models.

In this paper, we investigate robust parameter estimation and variable selection for binary regression models with grouped data. We investigate estimation procedures based on the minimum-distance approach. In particular, we employ minimum Hellinger and minimum symmetric chi-squared distances criteria and propose regularized minimum-distance estimators. These estimators appear to possess a certain degree of automatic robustness against model misspecification and/or for potential outliers. We show that the proposed non-penalized and penalized minimum-distance estimators are efficient under the model and simultaneously have excellent robustness properties. We study their asymptotic properties such as consistency, asymptotic normality and oracle properties. Using Monte Carlo studies, we examine the small-sample and robustness properties of the proposed estimators and compare them with traditional likelihood estimators. We also study two real-data applications to illustrate our methods. The numerical studies indicate the satisfactory finite-sample performance of our procedures.

A 13-Channel 1.53-mW 11.28-mm2 Electrical Impedance Tomography SoC Based on Frequency Division Multiplexing for Lung Physiological Imaging.

An electrical impedance tomography (EIT) system based on frequency division multiplexing (FDM) is proposed for real-time lung physiological imaging. The FDM technique allows the integration of 13 dedicated voltage sensing channels by combining data on-chip and sharing of ADC to alleviate area penalty caused by multi-channel. The EIT system-on-chip (SoC) is of the following features. 1) Early I/Q demodulation to relax the bandwidth requirement of analog front end and minimize the impact of motion artifacts and dc electrode offset. 2) Eliminates the need of adaptive gain control with constant inverted "U-shape" gain configuration to compensate amplitude variations across all channels. 3) FDM to combine 13 pairs of I/Q signals into two data streams for quantization using only two ΔΣ modulators. 4) Batch data recovery by Blackman window corrected fast Fourier transform without any digital filtering involved. 5) Lowest power consumption and smallest area occupation per channel reported to date. The EIT SoC occupies an area of 11.28 mm2 in 130-nm CMOS technology with a total power consumption of 1.53 mW under 1-V power supply. As a result, it generates lung EIT images at up to five frames per second.