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ObjectivesTo differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.MethodsThis retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman’s correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer–Lemeshow test.ResultsTo differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965–1) and 0.910 (95% CI: 0.830–0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness.ConclusionA nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

Background Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.Methods This retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis.Results A total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168–7.904), hematuria (OR = 3.656, 95%CI 1.325–10.083), CYS-C (OR = 4.416, 95%CI 2.296–8.491), and CA-125 (OR = 3.93, 95%CI 1.436–10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650–0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941–0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049).Conclusions Microalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments.

Oral infection of C57BL/6J mice with Toxoplasma gondii results in a marked bacterial dysbiosis and the development of severe pathology in the distal small intestine that is dependent on CD4+ T cells and interferon gamma (IFN-γ). This dysbiosis and bacterial translocation contribute to the development of ileal pathology, but the factors that support the bloom of bacterial pathobionts are unclear. The use of microbial community profiling and shotgun metagenomics revealed that Toxoplasma infection induces a dysbiosis dominated by Enterobacteriaceae and an increased potential for nitrate respiration. In vivo experiments using bacterial metabolic mutants revealed that during this infection, host-derived nitrate supports the expansion of Enterobacteriaceae in the ileum via nitrate respiration. Additional experiments with infected mice indicate that the IFN-γ/STAT1/iNOS axis, while essential for parasite control, also supplies a pool of nitrate that serves as a source for anaerobic respiration and supports overgrowth of Enterobacteriaceae Together, these data reveal a trade-off in intestinal immunity after oral infection of C57BL/6J mice with T. gondii, in which inducible nitric oxide synthase (iNOS) is required for parasite control, while this host enzyme is responsible for specific modification of the composition of the microbiome that contributes to pathology.IMPORTANCEToxoplasma gondii is a protozoan parasite and a leading cause of foodborne illness. Infection is initiated when the parasite invades the intestinal epithelium, and in many host species, this leads to intense inflammation and a dramatic disruption of the normal microbial ecosystem that resides in the healthy gut (the so-called microbiome). One characteristic change in the microbiome during infection with Toxoplasma-as well as numerous other pathogens-is the overgrowth of Escherichia coli or similar bacteria and a breakdown of commensal containment leading to seeding of peripheral organs with gut bacteria and subsequent sepsis. Our findings provide one clear explanation for how this process is regulated, thereby improving our understanding of the relationship between parasite infection, inflammation, and disease. Furthermore, our results could serve as the basis for the development of novel therapeutics to reduce the potential for harmful bacteria to bloom in the gut during infection.

In order to solve the problem of path planning for the spacecraft cluster to reach the dynamic target point under the premise of considering obstacle avoidance. In view of the fixed search radius, it will be difficult for the spacecraft to find a better value when it is close to the target point. This paper converts the orbital dynamics of each member spacecraft into an optimization problem considering constraints, and proposes an improved CPSO algorithm based on coordination. The path planning method of the traditional particle swarm optimization (CPSO): The dynamic radius search method that changes the search radius by changing the distance between them, and improves the CPSO algorithm based on this. The improved CPSO algorithm autonomously finds the optimal path of each member spacecraft at the current moment through the dynamic search radius, thereby obtaining the optimal solution for the dynamic path planning of the spacecraft cluster in three-dimensional space. The simulation results show that the use of the improved CPSO algorithm can not only obtain the optimal solution to the spacecraft cluster dynamic path planning problem, but also greatly reduce the fuel consumption in its path planning and improve the path stability of each member spacecraft.

In the presence of compound disturbances, a multi-spacecraft cooperative collision avoidance capture control method based on disturbance observer was proposed, which can solve the problem of low speed rolling non-cooperative target close-range capture in space. Firstly, a relative motion model of attitude and orbit coupling is established. Secondly, the disturbance observer is used to estimate and cancel the compound disturbance in the capture process. At the same time, the hyperquadric surfaces are used to describe the shape of space non-cooperative targets and capture spacecraft to establish a composite artificial potential field, and a robust control law with collision avoidance function is also designed. Finally, the stability of the controlled system is proved by using Lyapunov function, and the collision avoidance performance of the system is analyzed. Numerical simulations are carried out to evaluate the effectiveness of the proposed control scheme.