Air Force Institute of Aviation Medicine Affiliated Hospital

ABSTRACTDepression is a common comorbidity in CKD patients. The purpose of this study was to define the age‐specific association between the dietary inflammatory index (DII) and depression among patients with chronic kidney disease (CKD). Data in this cross‐sectional study were collected from the 2005–2018 National Health and Nutritional Examination Survey (NHANES). Weighted multivariable logistic regression models were conducted to assess the relationship between DII and depression in CKD patients across different age groups. A mediation analysis was constructed to clarify the mediating role of inflammation and renal function. A total of 5501 CKD patients were enrolled in this study. A significant association between DII and depression was demonstrated in patients with CKD aged ≤ 70 years, and a linear correlation was uncovered (p for nonlinear > 0.05). However, no significant effect was present in patients aged > 70 years. Additionally, residual renal function slightly mediated the role of DII on depression in CKD patients. DII was associated with depressive disorder in CKD patients partially through renal function. Younger CKD patients were more affected, and targeting age‐specific anti‐inflammatory diets could be an intervention strategy to prevent depression in patients with CKD.

The generalization capability of the decision-making modules in unmanned ground vehicles (UGVs) is critical for their safe deployment in unseen environments. Prevailing evaluation methods, which rely on aggregated performance over static benchmark sets, lack the granularity to diagnose the root causes of model failure, as they often conflate the distinct influences of scenario similarity and intrinsic difficulty. To overcome this limitation, we introduce a fine-grained, dynamic evaluation framework that deconstructs generalization along the dual axes of multi-level difficulty and similarity. First, scenario similarity is quantified through a four-layer hierarchical decomposition, with results aggregated into a composite similarity score. Test scenarios are independently classified into ten discrete difficulty levels via a consensus mechanism integrating large language models and task-specific proxy models. By constructing a three-dimensional (3D) performance landscape across similarity, difficulty, and task performance, we enable detailed behavioral diagnosis. The framework assesses robustness by analyzing performance within the high-similarity band (90–100%), while the full 3D landscape characterizes generalization under distribution shift. Seven interpretable metrics are derived to quantify distinct facets of both generalization and robustness. This initial validation focuses on the path-planning layer under full state observability, establishing a foundational proof-of-concept for the framework. It not only ranks algorithms but also reveals non-trivial behavioral patterns, such as the decoupling between in-distribution robustness and out-of-distribution generalization. It provides a reliable and interpretable foundation for evaluating the readiness of UGVs for safe deployment in unseen environments.

ObjectiveTo establish a predictive model for pregnancy outcomes in patients undergoing assisted reproductive technology (ART) treatment using human chorionic gonadotropin (HCG) day endometrial thickness (EMT), follicle output rate (FORT), serum anti-Müllerian hormone (AMH), and clinical characteristics.MethodsA retrospective study was conducted on 200 patients who underwent ART treatment at our hospital from August 2019 to August 2024. All patient data were obtained from the electronic medical record system, and patients were divided into a clinical pregnancy group and a non-pregnancy group based on whether they achieved pregnancy. Clinical-related data were collected and compared. Two machine learning methods, Lasso regression and Extreme Gradient Boosting (XGBoost), were used for “overlap coverage” screening of risk factors. The XGBoost model was implemented using the “xgboost” package in R, with the following hyperparameters: max_depth = 6, learning_rate = 0.3, n_estimators = 100, objective = “binary:logistic.” These parameters were selected based on common practices for binary classification tasks and were not extensively tuned due to the sample size limitations. Multivariate logistic analysis was performed to identify risk factors for pregnancy outcomes in ART patients. A regression model was established using R software for prediction and validation.ResultsA total of 200 ART patients were included in this study. According to medical record system records, 109 patients did not achieve pregnancy, accounting for 54.50%, and were included in the non-pregnancy group. 91 patients achieved clinical pregnancy, accounting for 45.50%, and were included in the clinical pregnancy group. There were significant statistical differences between the non-pregnant group and the clinically pregnant group in terms of age, body mass index, number of oocytes retrieved, HCG day EMT, AMH, and FORT (p < 0.05); Using LASSO regression and XGBoost machine learning, different risk factors were identified. Through the “overlap coverage” method, six common risk factors for pregnancy outcomes in ART patients were identified and included in the logistic regression model. The results indicated that age and body mass index were both risk factors influencing pregnancy outcomes in ART patients (OR = 1.196, 1.777, p < 0.05); the number of retrieved oocytes, HCG day EMT, AMH, and FORT were protective factors influencing pregnancy outcomes in ART patients (OR = 0.366, 0.382, 0.182, 0.862, p < 0.05); Based on the results of the logistic regression analysis, a risk prediction model for pregnancy outcomes in ART patients was constructed using a nomogram. The ROC curve showed an AUC value of 0.911, with a 95% confidence interval (CI) of 0.871 to 0.951. The model was validated using the Bootstrap method with 1,000 repeated samples, yielding a Cox-Snell R2 of 0.629, Nagelkerke R2 was 0.471, Brier Score was 0.117, model fit p-value was 0.240, and the statistic was 10.369. This indicates that the model’s predictive results align well with the actual occurrence of pregnancy outcomes in patients, with the clinical decision curve generally exceeding the two extreme curves. This suggests that the factors included in the nomogram provide a high net benefit for predicting pregnancy outcomes in ART-treated patients.ConclusionThere are numerous factors influencing pregnancy outcomes in ART patients, primarily including age, body mass index, number of oocytes retrieved, HCG day EMT, AMH, and FORT. The risk prediction nomogram model constructed based on these factors has certain predictive value for pregnancy outcomes in ART patients. Clinicians should identify high-risk populations early and optimize treatment plans accordingly.

This study used expert assessment, consumer perception, and noninvasive measurements of skin physiological parameters to develop a mathematical model for characterizing facial skin laxity in Chinese females, providing the scientific basis for personalized beauty and skin-firming strategies. The study involved 64 Chinese females (18–60 years) whose facial regions were imaged using VISIA-CR. Five experts assessed facial skin laxity using VISIA-CR images, while 72 consumers compared paired images to identify higher laxity. Noninvasive measurements of skin elasticity and other parameters were taken. The data were analyzed using Bradley-Terry analysis and support vector machine regression modeling. Consistent with expectations, facial skin laxity was negatively correlated with skin elasticity R2 values and positively correlated with skin elasticity F4 values. A novel Facial Skin Laxity Index (FSLI) was introduced to evaluate product efficacy in reducing laxity. Facial skin laxity can be characterized and quantified using a combination of expert assessments, consumer perceptions, and noninvasive measurements. The FSLI enables precise evaluation of product performance in improving facial skin laxity.

A high‐resolution autostereoscopic three‐dimensional (3D) display is proposed. It comprises two light source arrays (LSAs), a transparent liquid crystal display (LCD) panel, a scattering layer, and a lenticular lens. Light emitted from the LSAs is directed onto the transparent LCD panel, which projects the opening units corresponding to different parallax image pixels into distinct spatial locations, thereby creating new pixels. These new pixels are modulated and reconstructed by the scattering layer, incorporating both the original parallax image pixel information and the information from adjacent pixels post‐differentiation. Consequently, when the new pixels are projected through the lenticular lens to the viewpoint, a high‐resolution 3D image is displayed. The experimental results demonstrate the validity of our proposed autostereoscopic 3D display.

By introducing the loading and launching process, theoretical analysis and simulation experiments of the rocket projectile, the influence of the blocking force on the initial velocity, rotational speed, direction and pitch angular displacement of the rocket projectile was explored. By establishing the characteristic equation and interior ballistic model of the rocket projectile, it was concluded that the characteristic quantity of the initial disturbance of the rocket projectile was locking force, it had a significant impact on the direction and pitch angular displacement of the rocket projectile. The distribution of the impact point of the rocket projectile under the condition of the locking force of N was quantitatively represented by using the normal distribution with (X, Y) as the mathematical expectation. The results show that the locking force is a sudden force. The increase of the locking force will lead to the increase of the dispersion of the impact point and the decrease of the shooting density. Under the condition of meeting the locking performance requirements, it should be reduced as much as possible.

This study investigates the effects of acute hypobaric hypoxia (HH) on the anti-fatigue properties of pitolisant and explores the underlying mechanisms. The aim is to provide a theoretical basis for expanding its clinical indications and optimizing its use in individuals exposed to HH conditions. The anti-fatigue effects of pitolisant were evaluated using the water maze, novel object recognition, and rotating rod tests. Drug concentrations and dopamine levels were analyzed using High-Performance Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (HPLC-MS/MS). Additionally, gene and protein expression levels of P-glycoprotein (P-gp) and organic cation transporter 1 (OCT1) were assessed to explore the mechanisms by which HH affects pitolisant's pharmacodynamics. A 40mg/kg dose of pitolisant significantly improved learning, memory, cognitive, and motor functions in sleep-deprived mice under HH conditions (P < 0.05). Pharmacokinetic analysis revealed a reduction in pitolisant concentration in the brain under HH conditions. Furthermore, OCT1 protein expression decreased after 1h and 1 day of HH exposure (P < 0.05), while P-gp expression decreased after 1h (P < 0.05). HH possibly reduced pitolisant's brain concentration and efficacy by altering the expression of OCT1 and P-gp transporters. A 40mg/kg dose was necessary for an effective anti-fatigue response. Pitolisant shows potential for supporting circadian rhythm regulation in shift workers and individuals suffering from jet lag. When used under HH conditions, adjusting the dose and frequency may be necessary due to altered pharmacokinetics.