Early postoperative acetylsalicylic acid administration does not increase the risk of postoperative intracranial bleeding in patients with spontaneous intracerebral hemorrhage.

Administration of acetylsalicylic acid (ASA) at early stage after surgery for spontaneous intracerebral hemorrhage (SICH) may increase the risk of postoperative intracranial bleeding (PIB), because of potential inhibition of platelet function. This study aimed to investigate whether early ASA administration after surgery was related to increased risk of PIB. This retrospective study enrolled SICH patients receiving surgery from September 2019 to December 2022 in seven medical institution. Based on postoperative ASA administration, patients who continuously received ASA more than three days within seven days post-surgery were identified as ASA users, otherwise as non-ASA users. The primary outcome was symptomatic PIB events within seven days after surgery. Incidence of PIB was compared between ASA users and non-ASA users using survival analysis. This study included 744 appropriate patients from 794 SICH patients. PIB occurred in 42 patients. Survival analysis showed no statistical difference between ASA users and non-ASA users in incidence of PIB (P = 0.900). Multivariate Cox analysis demonstrated current smoker (hazard ratio [HR], 2.50, 95%CI, 1.33-4.71, P = 0.005), dyslipidemia (HR = 3.03; 95%CI, 1.31-6.99; P = 0.010) and pre-hemorrhagic antiplatelet therapy (HR = 3.05; 95% CI, 1.64-5.68; P < 0.001) were associated with PIB. Subgroup analysis manifested no significant difference in incidence of PIB between ASA users and non-ASA users after controlling the effect from factors of PIB (i.e., sex, age, current smoker, regular drinker, dyslipidemia, pre-hemorrhagic antiplatelet therapy and hematoma location). This study revealed that early ASA administration to SICH patients after surgery was not related to increased risk of PIB.

The difference of functional MR imaging in evaluating outcome of patients with diffuse and compact brain arteriovenous malformation.

Microsurgical resection is an effective method to treat brain arteriovenous malformations (BAVMs). Functional magnetic resonance imaging (fMRI) can evaluate the spatial relationship of nidus and eloquent. Diffuse BAVMs are related to poor outcomes postoperatively. The role of fMRI in evaluating outcomes in patients with different nidus types remains unclear. BAVM patients received microsurgical resection were included from a prospective, multicenter cohort study. All patients underwent fMRI evaluation preoperatively and were regularly followed up postoperatively. Diffuse BAVM is radiologically identified as nidus containing normal brain tissue interspersing between malformed vessels. Lesion-to-eloquent distance (LED) was calculated based on the relationship between nidus and eloquent. The primary outcome was 180-day unfavorable neurological status postoperatively. The risk of primary outcome was investigated within different BAVM nidus types. The LED's performance to predict poor outcome was evaluated using area under curve (AUC). 346 BAVM patients were included in this study. 93 (26.9%) patients were found to have a 180-day unfavorable outcome. Multivariate logistic analysis demonstrated LED (odd ratio [OR], 0.44; 0.34-0.57; P < 0.001) and mRS at admission (OR, 2.59; 1.90-3.54; P < 0.001) as factors of unfavorable outcome. Subgroup analysis showed LED and mRS at admission as factors of unfavorable outcome for patients with compact BAVMs (all P < 0.05), but not for patients with diffuse BAVMs. Subsequent analysis showed that LED performed poorly to predict the unfavorable outcome for patients with diffuse BAVMs, compared with patients with compact BAVMs (AUC as 0.69 vs. 0.86, P < 0.05). A larger cutoff value of LED to unfavorable outcome was found in patients with diffuse BAVMs (15 mm) compared with patients with compact BAVMs (4.7 mm). Usage of LED to evaluate postoperative outcome of patients with diffuse BAVMs differs from its use in patients with compact BAVMs. Specific assessment strategy considering BAVM nidus types could help improve patients' outcome. MITASREAVM cohort (unique identifier: NCT02868008, https://clinicaltrials.gov/study/NCT02868008?term=NCT02868008&rank=1 ).

Quantitative evaluation of acid flow behavior in fractures and optimization of design parameters based on acid wormhole filtration losses.

The technique of matrix acidification or acid fracturing is commonly utilized to establish communication with natural fractures during reservoir reconstruction. However, this process often encounters limitations due to filtration, which restricts the expansion of the primary acid-etching fracture. To address this issue, a computational model has been developed to simulate the expansion of an acid-etching wormhole by considering various factors such as formation process, injection duration, pressure build-up, and time-varying acid percolation rate. By analyzing the pumping displacement of acid-etching wormholes, this model provides valuable insights into the time-dependent quantities of acid percolation. It has been revealed that the filtration rate of acid-etching wormholes is strongly influenced by pumping displacement, viscosity, and concentration of the acid fluid used in stimulation as well as physical properties of the reservoir itself. Notably, viscosity plays a significant role in determining the effectiveness of acid fracturing especially in low-viscosity conditions. Acid concentration within 15% to 20% exhibits maximum impact on successful acid fracturing while concentrations below 15% or above 20% show no obvious effect. Furthermore, it was found that pumping displacement has a major influence on effective fracturing. However, beyond a certain threshold (> 5.0 m3/min), increased pumping displacement leads to slower etching distance for acids used in construction purposes. The simulation also provides real-time distribution analysis for acidity levels within eroded fractures during matrix-acidification processes and quantifies extent of chemical reactions between acids and rocks within these fractures thereby facilitating optimization efforts for design parameters related to matrix-acidification.

Algorithmic detection of sleep-disordered breathing using respiratory signals: a systematic review.

Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.