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Background: Frailty is a risk factor for acute myocardial infarction (AMI). This study examined the association between the modified frailty index (MFI) and adverse outcomes in patients with critical AMI.Methods: Data were obtained from the Medical Information Mart for Intensive Care IV database. Logistic and Cox regression models and a competing risk model were applied.Results: Of 5003 patients, 1496 were non-frail and 3507 were frail. Frailty was significantly associated with in-hospital mortality (per point, OR 1.13, 95% CI: 1.05– 1.21; frail vs non-frail, OR 1.31, 95% CI: 1.04– 1.65) and 1-year mortality (per point, HR 1.15, 95% CI: 1.11– 1.20; frail vs non-frail, HR 1.37, 95% CI: 1.20– 1.58). Frailty was significantly associated with post-discharge care needs (per point, OR 1.23, 95% CI: 1.14– 1.33; frail vs non-frail, OR 1.47, 95% CI: 1.22– 1.78). In the competing risk models, frailty was significantly associated with a lower probability of being discharged from the ICU (per point, HR 0.87, 95% CI: 0.85– 0.90; frail vs non-frail, HR 0.73, 95% CI: 0.68– 0.79) and hospital (per point, HR 0.82, 95% CI: 0.80– 0.85; frail vs non-frail, HR 0.62, 95% CI: 0.57– 0.68). Subgroup analyses showed the association of frailty with in-hospital and 1-year mortality was stronger in patients with a SOFA score ≤ 2 than in those with a SOFA score > 2 (both p< 0.05 for interaction).Conclusion: Frailty assessed by the MFI was an independent predictor of adverse outcomes in patients with critical AMI and may be helpful for prognostic risk stratification. [ABSTRACT FROM AUTHOR]

Time-space four-dimensional motion target localization is a fundamental and challenging task in the field of intelligent driving, and an important part of achieving the upgrade in existing target localization technologies. In order to solve the problem of the lack of localization of moving targets in a spatio-temporal four-dimensional environment in the existing spatio-temporal data model, this paper proposes an optical imaging model in the four-dimensional time-space system and a mathematical model of the object-image point mapping relationship in the four-dimensional time-space system based on the central perspective projection model, combined with the one-dimensional "time" and three-dimensional "space". After adding the temporal dimension, the imaging system parameters are extended. In order to solve the nonlinear mapping problem of complex systems, this paper proposes to construct a time-space four-dimensional object-image mapping relationship model based on a BP artificial neural network and demonstrates the feasibility of the joint time-space four-dimensional imaging model theory. In addition, indoor time-space four-dimensional localization prediction experiments verify the performance of the model in this paper. The maximum relative error rates of the predicted motion depth values, time values, and velocity values of this localization method compared with the real values do not exceed 0.23%, 2.03%, and 1.51%, respectively [ABSTRACT FROM AUTHOR]

Camera calibration is a key problem for 3D reconstruction in computer vision. Existing calibration methods, such as traditional, active, and self-calibration, all need to solve the internal and external parameters of the imaging system to clarify the image-object mapping relationship. The artificial neural network, which is based on connectionist architecture, provides a novel idea for the calibration of nonlinear mapping vision systems. It can learn the image-object mapping relationship from some sample points without considering too many uncertain factors in the middle. This paper discusses the learning ability. A binocular stereo-vision mapping model is used as the learning model to explore the ability of image-object mapping for artificial neural networks. This paper constructs sample libraries by pixel and world coordinates of checkerboard corners, builds the artificial neural network, and, through the training samples and test samples prediction, verifies the learning performance of the network. Furthermore, by the laser scanning binocular vision device constructed in the authors' laboratory and trained-well network, the 3D point cloud reconstruction of a physical target is performed. The experimental results show that the artificial neural network can learn the image-object mapping relationship well and more effectively avoid the impact of lens distortion and achieve more accurate nonlinear mapping at the edge of the image. When the X and Y coordinates are in the range of 100 mm and the Z coordinates are in the range of a 1000 mm, the absolute error rarely exceeds 2.5 mm, and the relative error is in the level of 10 -3 ; for 1000 mm distance measurement, the standard deviation does not exceed 1.5 mm. Network parameter selection experiments show that, for image-object mapping, a three-layer network and increasing the number of hidden layer's nodes can improve the training time more significantly.

Taurine has the function of immune regulation, relieving acute and chronic inflammation caused by various agents, and maintaining cell homeostasis. This investigation focused on the protective functions of taurine targeting acute lung injury (ALI) induced by LPS. Sixty male SD rats aged 6-7 weeks were segregated at random: blank control group (C group), taurine control group (T group), ALI model group (LPS group), and taurine prevention groups (LPST1, LPST, LPST3 Groups). C group and LPS group were given normal drinking water, while T group and LPST group were given 2% taurine in drinking water. LPST1 group was given 1% taurine in drinking water while. LPST3 group was given 3% taurine in drinking water. On the 14th and 28th day, LPS group and LPST1, LPST, and LPST3 groups were subjected to injection of LPS (25 mg/kg) into the trachea of rats. Serum, peripheral blood, lung tissue, and bronchoalveolar lavage fluid (BALF) were collected at 6 h post-LPS injection. The wet/dry ratio (W/D) of lung was measured by hot drying method. The population of white blood cells and the abundance of inflammatory-related cells within peripheral blood were counted by an automatic blood cell analyzer. The population of white blood cells within BALF was counted by a white blood cell counting plate combined with Swiss Giemsa staining, while the proportion of related white blood cells was calculated. BCA reagent was used to determine the protein concentration in BALF. The levels of pro-inflammatory factors (IL-1 β, IL-6, IL-18, TNF - α), anti-inflammation factors (IL-10, IL-4), and taurine within serum and lung tissue were detected by ELISA. Lung structural tissue alterations were observed through HE staining techniques. Myeloperoxidase (MPO) activities within lung tissue were detected through colorimetry. Protein expression levels of TLR4, MyD88, NF-κ Bp65, NF-κ Bp-p65, MCP-1, together with CD68 within lung tissue, were analyzed by Western blot (WB) and immunohistochemistry (IHC). The taurine pretreatment group contained significantly reduced W/D, MPO activity, and the number of inflammatory cells in BALF induced by LPS. In addition, compared with ALI model group, the taurine pretreatment group contained significantly reduced levels of pro-inflammatory factors in lung tissue, increased levels of anti-inflammatory factors, and decreased expression levels of key proteins in TLR-4/NF-κ B pathway. Taurine can protect rats from ALI by inhibiting the activation of neutrophils, macrophages, and TLR-4/NF-κ B signaling pathway., (© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.)