Background This longitudinal study aims to characterize longitudinal body mass index ( BMI ) trajectories during young adulthood (20–40 years) and examine the impact of level‐independent BMI trajectories on hypertension risk. Methods and Results The cohort consisted of 3271 participants (1712 males and 1559 females) who had BMI and blood pressure ( BP ) repeatedly measured 4 to 11 times during 2004 to 2015 and information on incident hypertension. Four distinct trajectory groups were identified using latent class growth mixture model: low‐stable (n=1497), medium‐increasing (n=1421), high‐increasing (n=291), sharp‐increasing (n=62). Model‐estimated levels and linear slopes of BMI at each age point between ages 20 and 40 were calculated in 1‐year intervals using the latent class growth mixture model parameters and their first derivatives, respectively. Compared with the low‐stable group, the hazard ratios and 95% CI were 2.42 (1.88, 3.11), 4.25 (3.08, 5.87), 11.17 (7.60, 16.41) for the 3 increasing groups, respectively. After adjusting for covariates, the standardized odds ratios and 95% CI of model‐estimated BMI level for incident hypertension increased in 20 to 35 years, ranging from 0.80 (0.72–0.90) to 1.59 (1.44–1.75); then decreased gradually to 1.54 (1.42–1.68). The standardized odds ratio s of level‐adjusted linear slopes increased from 1.22 (1.09–1.37) to 1.79 (1.59–2.01) at 20 to 24 years; then decreased rapidly to 1.12 (0.95–1.32). Conclusions These results indicate that the level‐independent BMI trajectories during young adulthood have significant impact on hypertension risk. Age between 20 and 30 years is a crucial period for incident hypertension, which has implications for early prevention.

Background Lung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules. Methods A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer‐Lemeshow test in both the training and validation cohorts. Results A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’ age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793‐0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745‐0.872). The Hosmer‐Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram. Conclusions The established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.

There is accumulating evidence that mitochondrial dysfunction is associated with the contribution of diabetes to Alzheimer’s disease (AD) progression. Neuronal mitochondrial proteins found in plasma neuronal-derived exosomes(NDEs) at levels that reflect those in brain neurons. Here, we tested the performance of mitochondrial proteins in plasma NDEs to predict cognitive decline and brain injury in diabetic participants. The type 2 diabetes mellitus (T2DM) participants in the study included 41 cognitively normal controls, 97 individuals with mild cognitive impairment (MCI) (68 individuals with stable MCI; 29 individuals with progressive MCI) and 36 patients with AD dementia. Plasma neuroexosomal proteins were measured by ELISA kits. Spearman’s correlation was used to test associations between plasma neuroexosomal mitochondrial proteins and other core biomarkers of AD. Diagnostic accuracy for progressive MCI and AD was obtained for mitochondrial proteins using receiver operating curve (ROC) analyses. The associations of mitochondrial proteins with the conversion from MCI to AD were assessed by Cox proportional hazard regression analysis. Plasma neuroexosomal NADH ubiquinone oxidoreductase core subunit S3 (NDUFS3) and succinate dehydrogenase complex subunit B (SDHB) levels were significantly lower in T2DM patients with AD dementia and progressive MCI than in cognitively normal subjects (P < 0.001 for both groups). We also found that plasma neuroexosomal NDUFS3 and SDHB levels were lower in progressive MCI than in stable MCI subjects. Both plasma neuroexosomal NDUFS3 and SDHB offer diagnostic utility for AD. Low plasma neuroexosomal SDHB levels significantly predicted conversion from MCI to AD. In addition, low mitochondrial proteins levels were associated with the rate of hippocampal and gray matter atrophy, reduced AD signature cortical thickness in progressive MCI over the follow-up period. These data suggest that both plasma neuroexosomal NDUFS3 and SDHB are already increased at the early clinical stage of AD, and indicate the promise of plasma neuroexosomal mitochondrial proteins as diagnostic and prognostic biomarkers for the earliest symptomatic stage of AD in diabetic participants.

A terminal guidance control system for small fixed‐wing unmanned aerial vehicles (SUAV, with a wingspan of around 1.5 m) is proposed in this paper based on a visible light strap‐down seeker. The system is implemented using low‐cost, open‐source components with a cost less than $2000 and proved to be feasible, transplantable and effective for different types of SUAVs, achieving a good terminal guidance accuracy (circular error probable = 1.5 m) against a nonmaneuvering target on the ground. Impacts of low‐cost and low‐precision electrical modules mainly include influence on terminal guidance information, measurement of SUAV state values and the presented system's time‐delay. We examine the practical median flow algorithm for target tracking, the attitude pursuit guidance law for terminal guidance control and the pursuit image frame method for large delay interception to eliminate adverse effects. Furthermore, the performance and robustness are evaluated by laboratory tests as well as field flight trials on two different SUAV platforms. This paper provides a robust, effective and proven reference for the development of the aggressive SUAV for SUAV swarms.