Hypoxic acclimatization training improves the resistance to motion sickness

ObjectiveVestibular provocation is one of the main causes of flight illusions, and its occurrence is closely related to the susceptibility of motion sickness (MS). However, existing training programs have limited effect in improving the resistance to motion sickness. In this study, we investigated the effects of hypoxia acclimatization training (HAT) on the resistance to motion sickness.MethodsHealthy military college students were identified as subjects according to the criteria. MS model was induced by a rotary chair. Experimental groups included control, HAT, 3D roller training (3DRT), and combined training.ResultsThe Graybiel scores were decreased in the HAT group and the 3DRT group and further decreased in the combined training group in MS induced by the rotary chair. Participants had a significant increase in blood pressure after the rotary chair test and a significant increase in the heart rate during the rotary chair test, but these changes disappeared in all three training groups. Additionally, LFn was increased, HFn was decreased, and LF/HF was increased accordingly during the rotary chair test in the control group, but the changes of these three parameters were completely opposite in the three training groups during the rotary chair test. Compared with the control group, the decreasing changes in pupillary contraction velocity (PCV) and pupillary minimum diameter (PMD) of the three training groups were smaller. In particular, the binocular PCV changes were further attenuated in the combined training group.ConclusionOur research provides a possible candidate solution for training military pilots in the resistance to motion sickness

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Ensuring flight safety for unmanned aerial vehicles (UAVs) is a critical concern, necessitating effective mathematical modeling for safety forecasting in both academic and industrial contexts. This study addresses this need by combining the capabilities of deep neural networks and grey analysis to create a comprehensive mathematical modeling approach focused on spatial information service (SIS). The paper introduces a novel spatial information-aware flight security forecasting model for UAVs, emphasizing the transformative impact of the new methodology. Traditionally, factors influencing flight safety are identified and formulated based on SIS chain technology, SIS management rules, SIS business processes, and spatial SIS chain verification. To address the challenges posed by significant data volatility and missing data in the sample dateset, a non-equally spaced GM (1,1) model with an approximate non-simultaneous exponential law series is developed for prediction. Subsequently, multiple influencing factors are encoded and input into a specific BP neural network structure. The paper concludes with simulation experiments to evaluate the proposed model. The results of the simulation analysis demonstrate that the integration of deep learning and grey analysis in the proposed model effectively recognizes flight security risks with high efficiency. This underscores the transformative potential of the new approach in enhancing UAV flight safety forecasting

PIEZO1 Promotes the Migration of Endothelial Cells via Enhancing CXCR4 Expression under Simulated Microgravity

Exposure to microgravity during spaceflight induces the alterations in endothelial cell function associated with post-flight cardiovascular deconditioning. PIEZO1 is a major mechanosensitive ion channel that regulates endothelial cell function. In this study, we used a two-dimensional clinostat to investigate the expression of PIEZO1 and its regulatory mechanism on human umbilical vein endothelial cells (HUVECs) under simulated microgravity. Utilizing quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analysis, we observed that PIEZO1 expression was significantly increased in response to simulated microgravity. Moreover, we found microgravity promoted endothelial cells migration by increasing expression of PIEZO1. Proteomics analysis highlighted the importance of C-X-C chemokine receptor type 4(CXCR4) as a main target molecule of PIEZO1 in HUVECs. CXCR4 protein level was increased with simulated microgravity and decreased with PIEZO1 knock down. The mechanistic study showed that PIEZO1 enhances CXCR4 expression via Ca2+ influx. In addition, CXCR4 could promote endothelial cell migration under simulated microgravity. Taken together, these results suggest that the upregulation of PIEZO1 in response to simulated microgravity regulates endothelial cell migration due to enhancing CXCR4 expression via Ca2+ influx

Develop and Validate a Risk Score in Predicting Renal Failure in Focal Segmental Glomerulosclerosis

Introduction The aim of this study is to develop and validate a risk score for end stage kidney disease (ESKD) in patients with focal segmental glomerulosclerosis (FSGS). Methods Patient with biopsy proven FSGS were enrolled. All the patients were allocated 1:1 to the two groups according to their baseline gender, age and baseline creatinine level by using a stratified randomization method. ESKD was the primary endpoint. Results We recruited 359 FSGS patients,177 subjects were assigned to group 1 and 182 to group 2. The clinicopathological variables were similar between two groups. There were 23 (13%) subjects reached to ESKD in group 1 and 22 (12.1%) in group 2. By multivariate Cox regression analyses we established risk scores (RS) 1 and RS 2 in groups 1 and 2, respectively. RS1 consists of five parameters including lower eGFR, higher urine protein, MAP, IgG level, and tubular-interstitial lesion (TIL) score; RS2 also consists of five predictors including lower C3, higher MAP, IgG level, hemoglobin and TIL score. RS1 and RS2 were cross-validated between these two groups, showing RS1 had better performance in predicting 5-year ESKD in Group1 [c statics, 0.86(0.74-0.98) vs 0.82(0.69-0.95] and Group2 [c statics, 0.91(0.83-0.99) vs 0.89(0.79-0.99)] compared to RS2. We then stratified the risk factors into four groups and Kaplan-Meier survival curve revealed that patients progressed to ESKD increased as risk levels increased. Conclusions A predictive model incorporated clinicopathological features was developed and validated for the prediction of ESKD in FSGS patients