Trends in cardiovascular risk factor prevalence, treatment, and control among US adolescents aged 12 to 19 years, 2001 to March 2020.

BACKGROUND: Early-life cardiovascular risk factors (CVRFs) are known to be associated with target organ damage during adolescence and premature cardiovascular morbidity and mortality during adulthood. However, contemporary data describing whether the prevalence of CVRFs and treatment and control rates have changed are limited. This study aimed to examine the temporal trends in the prevalence, treatment, and control of CVRFs among US adolescents over the past 2 decades. METHODS: This is a serial cross-sectional study using data from nine National Health and Nutrition Examination Survey cycles (January 2001-March 2020). US adolescents (aged 12 to 19 years) with information regarding CVRFs (including hypertension, elevated blood pressure [BP], diabetes, prediabetes, hyperlipidemia, obesity, overweight, cigarette use, inactive physical activity, and poor diet quality) were included. Age-adjusted trends in CVRF prevalence, treatment, and control were examined. Joinpoint regression analysis was performed to estimate changes in the prevalence, treatment, and control over time. The variation by sociodemographic characteristics were also described. RESULTS: A total of 15,155 US adolescents aged 12 to 19 years (representing ≈ 32.4 million people) were included. From 2001 to March 2020, there was an increase in the prevalence of prediabetes (from 12.5% [95% confidence interval (CI), 10.2%-14.9%] to 37.6% [95% CI, 29.1%-46.2%]) and overweight/obesity (from 21.1% [95% CI, 19.3%-22.8%] to 24.8% [95% CI, 21.4%-28.2%]; from 16.0% [95% CI, 14.1%-17.9%] to 20.3% [95% CI, 17.9%-22.7%]; respectively), no improvement in the prevalence of elevated BP (from 10.4% [95% CI, 8.9%-11.8%] to 11.0% [95% CI, 8.7%-13.4%]), diabetes (from 0.7% [95% CI, 0.2%-1.2%] to 1.2% [95% CI, 0.3%-2.2%]), and poor diet quality (from 76.1% [95% CI, 74.0%-78.2%] to 71.7% [95% CI, 68.5%-74.9%]), and a decrease in the prevalence of hypertension (from 8.1% [95% CI, 6.9%-9.4%] to 5.5% [95% CI, 3.7%-7.3%]), hyperlipidemia (from 34.2% [95% CI, 30.9%-37.5%] to 22.8% [95% CI, 18.7%-26.8%]), cigarette use (from 18.0% [95% CI, 15.7%-20.3%] to 3.5% [95% CI, 2.0%-5.0%]), and inactive physical activity (from 83.0% [95% CI, 80.7%-85.3%] to 9.5% [95% CI, 4.2%-14.8%]). Sex and race/ethnicity affected the evolution of CVRF prevalence differently. Whilst treatment rates for hypertension and diabetes did not improve significantly (from 9.6% [95% CI, 3.5%-15.8%] to 6.0% [95% CI, 1.4%-10.6%]; from 51.0% [95% CI, 23.3%-78.7%] to 26.5% [95% CI, 0.0%-54.7%]; respectively), BP control was relatively stable (from 75.7% [95% CI, 56.8%-94.7%] to 73.5% [95% CI, 40.3%-100.0%]), while glycemic control improved to a certain extent, although it remained suboptimal (from 11.8% [95% CI, 0.0%-31.5%] to 62.7% [95% CI, 62.7%-62.7%]). CONCLUSIONS: From 2001 to March 2020, although prediabetes and overweight/obesity increased, hypertension, hyperlipidemia, cigarette use, and inactive physical activity decreased among US adolescents aged 12 to 19 years, whereas elevated BP, diabetes, and poor diet quality remained unchanged. There were disparities in CVRF prevalence and trends across sociodemographic subpopulations. While treatment and control rates for hypertension and diabetes plateaued, BP control were stable, and improved glycemic control was observed.

QKSAN: A Quantum Kernel Self-Attention Network.

The Self-Attention Mechanism (SAM) excels at distilling important information from the interior of data to improve the computational efficiency of models. Nevertheless, many Quantum Machine Learning (QML) models lack the ability to distinguish the intrinsic connections of information like SAM, which limits their effectiveness on massive high-dimensional quantum data. To tackle the above issue, a Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. Further, a Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques to release half of quantum resources by mid-circuit measurement, thereby bolstering both feasibility and adaptability. Simultaneously, the Quantum Kernel Self-Attention Score (QKSAS) with an exponentially large characterization space is spawned to accommodate more information and determine the measurement conditions. Eventually, four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST, where the QKSAS tests and correlation assessments between noise immunity and learning ability are executed on the best-performing sub-model. The paramount experimental finding is that the QKSAN subclasses possess the potential learning advantage of acquiring impressive accuracies exceeding 98.05% with far fewer parameters than classical machine learning models. Predictably, QKSAN lays the foundation for future quantum computers to perform machine learning on massive amounts of data while driving advances in areas such as quantum computer vision.

Pretrained Quantum-Inspired Deep Neural Network for Natural Language Processing.

Natural language processing (NLP) may face the inexplicable "black-box" problem of parameters and unreasonable modeling for lack of embedding of some characteristics of natural language, while the quantum-inspired models based on quantum theory may provide a potential solution. However, the essential prior knowledge and pretrained text features are often ignored at the early stage of the development of quantum-inspired models. To attacking the above challenges, a pretrained quantum-inspired deep neural network is proposed in this work, which is constructed based on quantum theory for carrying out strong performance and great interpretability in related NLP fields. Concretely, a quantum-inspired pretrained feature embedding (QPFE) method is first developed to model superposition states for words to embed more textual features. Then, a QPFE-ERNIE model is designed by merging the semantic features learned from the prevalent pretrained model ERNIE, which is verified with two NLP downstream tasks: 1) sentiment classification and 2) word sense disambiguation (WSD). In addition, schematic quantum circuit diagrams are provided, which has potential impetus for the future realization of quantum NLP with quantum device. Finally, the experiment results demonstrate QPFE-ERNIE is significantly better for sentiment classification than gated recurrent unit (GRU), BiLSTM, and TextCNN on five datasets in all metrics and achieves better results than ERNIE in accuracy, F1-score, and precision on two datasets (CR and SST), and it also has advantage for WSD over the classical models, including BERT (improves F1-score by 5.2 on average) and ERNIE (improves F1-score by 4.2 on average) and improves the F1-score by 8.7 on average compared with a previous quantum-inspired model QWSD. QPFE-ERNIE provides a novel pretrained quantum-inspired model for solving NLP problems, and it lays a foundation for exploring more quantum-inspired models in the future.