Joint effect of abnormal systemic immune-inflammation index (SII) levels and diabetes on cognitive function and survival rate: A population-based study from the NHANES 2011-2014.

OBJECTIVE: The purpose of this study was to investigate whether the combination of abnormal systemic immune-inflammation index (SII) levels and hyperglycemia increased the risk of cognitive function decline and reduced survival rate in the United States. METHODS: This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) database from 2011-2014 and enrolled 1,447 participants aged 60 years or older. Restricted cubic splines (RCS), linear regression and kaplan-meier(KM) curve were employed to explore the combined effects of abnormal SII and hyperglycemia on cognitive function and survival rate, and subgroup analysis was also conducted. RESULTS: The RCS analysis revealed an inverted U-shaped relationship between lgSII levels and cognitive function. Linear regression analysis indicated that neither abnormal SII nor diabetes alone significantly contributed to the decline in cognitive function compared to participants with normal SII levels and blood glucose. However, when abnormal SII coexisted with diabetes (but not prediabetes), it resulted to a significant decline in cognitive function. After adjusting for various confounding factors, these results remained significant in Delayed Word Recall (ß:-0.76, P<0.05) and Digit Symbol Substitution tests (ß:-5.02, P<0.05). Nevertheless, these results showed marginal significance in Total Word Recall test as well as Animal Fluency test. Among all subgroup analyses performed, participants with both abnormal SII levels and diabetes exhibited the greatest decline in cognitive function compared to those with only diabetes. Furthermore, KM curve demonstrated that the combination of abnormal SII levels and diabetes decreased survival rate among participants. CONCLUSION: The findings suggest that the impact of diabetes on cognitive function/survival rate is correlated with SII levels, indicating that their combination enhances predictive power.

Effects of elevated remnant cholesterol on outcomes of acute ischemic stroke patients receiving mechanical thrombectomy.

OBJECTIVE: Large cohort studies provided evidence that elevated remnant cholesterol (RC) was an important risk factor for ischemic stroke. However, the association between high RC and clinical outcomes in acute ischemic stroke (AIS) individuals was still undetermined. METHODS: This retrospective study enrolled 165 AIS patients undergoing mechanical thrombectomy in one tertiary stroke center. We divided patients into two groups based on the median of their RC levels (0.49 mmol/L). The modified Rankin Scale (mRS) was used to evaluate the primary outcome 90 days after the onset of symptoms. The mRS scores ≤ 2 and ≤ 1 at 90 days were deemed as favorable and excellent outcomes, respectively. RESULTS: In the overall AIS patients undergoing mechanical thrombectomy, there was no obvious distinction between the high and low RC group at 90-day favorable outcome (41.0% vs. 47.1%, P = 0.431) or excellent outcome (23.1% vs. 31.0%, P = 0.252). In the subgroup analysis stratified by stroke etiology, non-large artery atherosclerosis (non-LAA) stroke patients yielded with less favorable or excellent prognosis in the high RC group (26.8% vs. 46.8%, adjusted OR = 0.31, 95%CI: 0.11-0.85, P = 0.023; or 12.2% vs. 29.0%, adjusted OR = 0.18, 95%CI: 0.04-0.80, P = 0.024, respectively.). Post hoc power analyses indicated that the power was sufficient for favorable outcome (80.38%) and excellent outcome (88.72%) in non-LAA stroke patients. Additionally, RC can enhance the risk prediction value of a poor outcome (mRS scores 3-6) based on traditional risk indicators (including age, initial NIHSS score, operative duration, and neutrophil-to-lymphocyte ratio) for non-LAA stroke patients (AUC = 0.86, 95%CI: 0.79-0.94, P < 0.001). CONCLUSION: In AIS patients undergoing mechanical thrombectomy, elevated RC was independently related to poor outcome for non-LAA stroke patients, but not to short-term prognosis of LAA stroke patients.

A Lightweight Pre-Crash Occupant Injury Prediction Model Distills Knowledge From Its Post-Crash Counterpart.

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Resting-state electroencephalography microstate dynamics altered in patients with migraine with and without aura-A pilot study.

OBJECTIVE: To evaluate electroencephalography (EEG) microstate differences between patients with migraine with aura (MWA), patients with migraine without aura (MWoA), and healthy controls (HC). BACKGROUND: Previous research employing microstate analysis found unique microstate alterations in patients with MWoA; however, it is uncertain how microstates appear in patients with MWA. METHODS: This study was conducted at the Headache Clinic of the First Affiliated Hospital of Xi'an Jiaotong University. In total, 30 patients with MWA, 30 with MWoA, and 30 HC were enrolled in this cross-sectional study. An EEG was recorded for all participants under resting state. The microstate parameters of four widely recognized microstate classes A-D were calculated and compared across the three groups. RESULTS: The occurrence of microstate B (MsB) in the MWoA group was significantly higher than in the HC (p = 0.006, Cohen's d = 0.72) and MWA (p = 0.016, Cohen's d = 0.57) groups, while the contribution of MsB was significantly increased in the MWoA group compared to the HC group (p = 0.016, Cohen's d = 0.64). Microstate A (MsA) displayed a longer duration in the MWA group compared to the MWoA group (p = 0.007, Cohen's d = 0.69). Furthermore, the transition probability between MsB and microstate D was significantly increased in the MWoA group compared to the HC group (p = 0.009, Cohen's d = 0.68 for B to D; p = 0.007, Cohen's d = 0.71 for D to B). Finally, the occurrence and contribution of MsB were positively related to headache characteristics in the MWoA group but negatively in the MWA group, whereas the duration of MsA was positively related to the visual analog scale in the MWA group (all p < 0.05). CONCLUSIONS: Patients with MWA and MWoA have altered microstate dynamics, indicating that resting-state brain network disorders may play a role in migraine pathogenesis. Microstate parameters may have the potential to aid clinical management, which needs to be investigated further.

Post-dural puncture headache combined with pneumocephalus secondary to vaginal delivery following epidural anesthesia: a case report.

BACKGROUND: Pneumocephalus is rare in vaginal deliveries. Pneumocephalus may be asymptomatic or present with signs of increased intracranial pressure. However, parturients who received epidural anesthesia with air in their brains may experience low intracranial pressure headaches after giving birth, causing the diagnosis of pneumocephalus to be delayed. We report a case of a parturient who developed post-dural puncture headache combined with pneumocephalus secondary to vaginal delivery following epidural anesthesia. CASE PRESENTATION: A 24-year-old G1P0 Chinese woman at 38 weeks gestation was in labor and received epidural anesthesia using the loss of resistance to air technique and had a negative prior medical history. She presented with postural headache, neck stiffness and auditory changes 2 h after vaginal delivery. The head non-contrast computed tomography revealed distributed gas density shadows in the brain, indicating pneumocephalus. Her headache was relieved by bed rest, rehydration, analgesia, and oxygen therapy and completely disappeared after 2 weeks of postpartum bed rest. CONCLUSIONS: This is the first report that positional headaches after epidural anesthesia may not indicate low intracranial pressure alone; it may combine with pneumocephalus, particularly when using the loss of resistance to air technique. At this moment, head computed tomography is essential to discover other conditions like pneumocephalus.

Human injury-based safety decision of automated vehicles.

Automated vehicles (AVs) are anticipated to improve road traffic safety. However, prevailing decision-making algorithms have largely neglected the potential to mitigate injuries when confronting inevitable obstacles. To explore whether, how, and to what extent AVs can enhance human protection, we propose an injury risk mitigation-based decision-making algorithm. The algorithm is guided by a real-time, data-driven human injury prediction model and is assessed using detailed first-hand information collected from real-world crashes. The results demonstrate that integrating injury prediction into decision-making is promising for reducing traffic casualties. Because safety decisions involve harm distribution for different participants, we further analyze the potential ethical issues quantitatively, providing a technically critical step closer to settling such dilemmas. This work demonstrates the feasibility of applying mining tools to identify the underlying mechanisms embedded in crash data accumulated over time and opens the way for future AVs to facilitate optimal road traffic safety.

Kinetic and Kinematic Features of Pedestrian Avoidance Behavior in Motor Vehicle Conflicts.

The active behaviors of pedestrians, such as avoidance motions, affect the resultant injury risk in vehicle-pedestrian collisions. However, the biomechanical features of these behaviors remain unquantified, leading to a gap in the development of biofidelic research tools and tailored protection for pedestrians in real-world traffic scenarios. In this study, we prompted subjects ("pedestrians") to exhibit natural avoidance behaviors in well-controlled near-real traffic conflict scenarios using a previously developed virtual reality (VR)-based experimental platform. We quantified the pedestrian-vehicle interaction processes in the pre-crash phase and extracted the pedestrian postures immediately before collision with the vehicle; these were termed the "pre-crash postures." We recorded the kinetic and kinematic features of the pedestrian avoidance responses-including the relative locations of the vehicle and pedestrian, pedestrian movement velocity and acceleration, pedestrian posture parameters (joint positions and angles), and pedestrian muscle activation levels-using a motion capture system and physiological signal system. The velocities in the avoidance behaviors were significantly different from those in a normal gait (p < 0.01). Based on the extracted natural reaction features of the pedestrians, this study provides data to support the analysis of pedestrian injury risk, development of biofidelic human body models (HBM), and design of advanced on-vehicle active safety systems.

A data-driven, kinematic feature-based, near real-time algorithm for injury severity prediction of vehicle occupants.

Accurate real-time prediction of occupant injury severity in unavoidable collision scenarios is a prerequisite for enhancing road traffic safety with the development of highly automated vehicles. Specifically, a safety prediction model provides a decision reference for the trajectory planning system in the pre-crash phase and the adaptive restraint system in the in-crash phase. The main goal of the current study is to construct a data-driven, vehicle kinematic feature-based model to realize accurate and near real-time prediction of in-vehicle occupant injury severity. A large-scale numerical database was established focusing on occupant kinetics. A first-step deep-learning model was established to predict occupant kinetics and injury severity using a convolutional neural network (CNN). To reduce the computational time for real-time application, the second step was to extract simplified kinematic features from vehicle crash pulses via a feature extraction method, which was inspired by a visualization approach applied to the CNN-based model. The features were incorporated with a low-complexity machine-learning algorithm and achieved satisfactory accuracy (85.4 % on the numerical database, 78.7 % on a 192-case real-world dataset) and decreased computational time (1.2 ± 0.4 ms) on the prediction tasks. This study demonstrated the feasibility of using data-driven and feature-based approaches to achieve accurate injury risk estimation prior to collision. The proposed model is expected to provide a decision reference for integrated safety systems in the next generation of automated vehicles.