BackgroundThe relationship between a single food or nutrient and pulmonary tuberculosis (TB) has been explored in many studies; however, the relationship between dietary patterns and TB is still lacking.ObjectiveOur study aims to investigate the association between dietary patterns and the initial clinical manifestations in patients with TB.Materials and methodsA cross-sectional study including 1,661 patients with active TB was conducted in Qingdao, China, from 2011 to 2019. A semiquantitative food frequency questionnaire was used to collect dietary data. Dietary patterns were determined by principal component factor analysis. Initial clinical manifestations were assessed using a combination of the patient self-reported clinical symptoms and the admission results indicated by the TB score. The associations between dietary patterns and TB scores in patients with TB were examined by the logistics regression model.ResultsThe analysis identified four dietary patterns: meat-fruit-seafood pattern; dairy-egg pattern; beans and their products-whole grain pattern; and refined grain-vegetable pattern. In a multiple-adjusted model, higher adherence to the meat-fruit-seafood pattern showed a protective effect on the TB score (OR 0.53, 95% CI 0.39, 0.84, P for trend = 0.010) and the association was stronger in patients older than 45 years (OR 0.32, 95% CI 0.16, 0.64, P for trend < 0.001). The higher adherence to beans and their products-whole grain pattern was a protective factor for TB score (OR 0.57, 95% CI 0.37, 0.87, P for trend = 0.025), and the association was also observed in patients with concurrent TB and diabetes mellitus (DM) with a more significant effect (OR 0.33, 95% CI 0.14, 0.80, P for trend = 0.025). No significant association was found between dairy-egg pattern and refined grain–vegetable dietary pattern with TB score.ConclusionDietary patterns characterized by a balanced diet rich in high-quality protein, sufficient energy, as well as marine n-3 PUFA, phytochemicals, B vitamins, and fiber are associated with mild initial clinical manifestations, and the association is stronger in patients older than 45 years and those with concurrent TB and DM.
INTRODUCTION: With the continuous progress of the medical Internet of Things, intelligent medical wearable devices are also gradually mature. Among them, medical wearable devices for arrhythmia detection have broad application prospects. Arrhythmia is a common cardiovascular disease. Arrhythmia causes millions of deaths every year and is one of the most noteworthy diseases. Medical mobile information systems (MMIS) provide many ECG signals, which can be used to train deep models to detect arrhythmia automatically. OBJECTIVES: Using deep models to detect arrhythmia is a research hot spot. However, the current algorithms for arrhythmia detection lack of attention to the unsupervised depth model. And they usually build a large comprehensive model for all users for arrhythmia detection, which has low flexibility and cannot extract personalized features from users. Therefore, this paper proposes a personalized arrhythmia detection system based on attention mechanism called personAD. METHODS: The personAD contains four modules: (1) Preprocessing module; (2) Training module; (3) Arrhythmia detection module and (4) User registration module. The personAD trains a separate autoencoder for each user to detect personalized arrhythmia. Using autoencoder to detect arrhythmia can avoid the imbalance of training data. The autoencoder combines a convolutional network and two attention mechanisms. RESULTS: Based on the results on MIT-BIH Arrhythmia Database, we can find that our arrhythmia detection system achieve 98.03% and 99.32% respectively. CONCLUSION: The personAD can effectively detect arrhythmia in ECG signals. The personAD has higher flexibility, and can easily modify the autoencoders for detecting arrhythmia for users.
[Aims] The saline soil in continuous silting tidal areas will become a crucial reserved land resource in China. A prominent problem is controlling soil salinization for improving agricultural water and land resources’ productivity in coastal areas. [Methods] An experiment was conducted to study the effects of different mulching and tillage measures on soil salt-water status and maize growth. There were four treatments: (1) film mulching (FM), by only setting a transparent plastic film (with a thickness of 6 μm) on the surface soil; (2) straw deep-burying (SDB), in which only straw was buried as a layer at a soil depth of 30 cm; (3) combining film mulch with deep-buried straw (F+S), in which a straw layer was buried at a soil depth of 30 cm with plastic film mulching on the soil surface; and (4) control (CK), by simulating standard local practice. [Results] The results showed that the soil water storage (SWS) under FM and F+S was significantly higher than others, and F+S showed the best role in soil water conservation. The film mulching had a reasonable effect on soil salinity regulation during the whole maize growth stage; the soil salt content at 0–30 cm was decreased by 1 g/kg and 0.74 g/kg under F+S and FM, respectively. Compared to CK, the plant height, LAI, SPAD value, and yield were all improved under mulching and tillage. The growth process of maize and water-use efficiency (WUE) under F+S was more significantly improved than those under other treatments. [Conclusions] Overall, the F+S can be recommended as a suitable strategy for regulating soil salt and moisture, and thus improving crop productivity in coastal tidal areas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.