Search

The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p < 0.001) in both test sets. In an emulated randomized controlled trial (eRCT) on the Shanghai outdoor cohort (n = 3303) where DeepMyopia showed effectiveness in myopia prevention compared to NonCyc-based model, with an adjusted relative reduction (ARR) of -17.8%, 95% CI: -29.4%, -6.4%. DeepMyopia-assisted interventions attained quality-adjusted life years (QALYs) of 0.75 (95% CI: 0.53, 1.04) per person and avoided blindness years of 13.54 (95% CI: 9.57, 18.83) per 1 million persons compared to natural lifestyle with no active intervention. Our findings demonstrated DeepMyopia as a reliable and efficient AI-based decision support system for intervention guidance for children.

AIMS: To describe glucose-lowering treatment regimens and glycated haemoglobin (HbA1c) trajectories in individuals with type 2 diabetes (T2D) over 36 months of follow-up from the start of second-line therapy. MATERIALS AND METHODS: This data analysis from the 3-year, observational DISCOVER study programme included 14 687 participants from 37 countries with T2D initiating second-line glucose-lowering therapy. Treatment and HbA1c data were collected at baseline (start of second-line therapy) and at 6, 12, 24 and 36 months. Treatment regimen changes over follow-up were analysed using the McNemar test, with carry-forward imputation for intermediate missing values. RESULTS: A total of 11 592 participants had treatment data at baseline and 36 months, and 11 882 had HbA1c data at baseline. At baseline and 36 months, respectively, rates of oral monotherapy use were 12.1% and 12.4% (P = 0.22), rates of dual oral therapy use were 63.4% and 47.6% (P < 0.0001), rates of ≥ triple oral therapy use were 17.5% and 25.4% (P < 0.0001), and rates of injectable treatment use were 7.0% and 13.7% (P < 0.0001). Use of injectable drugs was most common among participants with an HbA1c level ≥64 mmol/mol (≥8.0%). Overall, 42.9% of participants changed treatment during follow-up. Mean HbA1c levels at baseline and 6 months were 67 mmol/mol (8.3%) and 55 mmol/mol (7.2%), respectively, remaining stable thereafter. CONCLUSIONS: Dual oral therapy was the most common treatment regimen at the start of second-line treatment, and over half of the participants remained on the same treatment during follow-up.

A potential record of Earth’s magnetic field going back 4.2 billion years (Ga) ago is carried by magnetite inclusions in zircon grains from the Jack Hills. This magnetite may be secondary in nature, however, meaning that the magnetic record is much younger than the zircon crystallization age. Here, we use atom probe tomography to show that Pb-bearing nanoclusters in magnetite-bearing Jack Hills zircons formed during two discrete events at 3.4 and <2 Ga. The older population of clusters contains no detectable Fe, whereas roughly half of the younger population of clusters is Fe bearing. This result shows that the Fe required to form secondary magnetite entered the zircon sometime after 3.4 Ga and that remobilization of Pb and Fe during an annealing event occurred more than 1 Ga after deposition of the Jack Hills sediment at 3 Ga. The ability to date Fe mobility linked to secondary magnetite formation provides new possibilities to improve our knowledge of the Archean geodynamo.

This article investigates the finite-time and fixed-time synchronization for memristive neural networks (MNNs) with mixed time-varying delays under the adaptive aperiodically intermittent adjustment strategy. Different from previous works, this article first employs the aperiodically intermittent adjustment feedback control and adaptive control to drive the MNNs to achieve synchronization in finite time and fixed time. First of all, according to the theories of set-valued mappings and differential inclusions, the error MNNs is derived, and its finite-time and fixed-time stability problems are discussed by applying the Lyapunov function method and some LMI techniques. Moreover, by meticulously designing an effective aperiodically intermittent adjustment with adaptive updating law, sufficient conditions that guarantee the finite-time and fixed-time synchronization of the drive-response MNNs are obtained, and the settling time is explicitly estimated. Finally, three numerical examples are provided to illustrate the validity of the obtained theoretical results.

Mixed metal sulfides (MMSs) are highly noticeable as prospective materials for sodium storage due to their prominent electrochemical reversibility and excellent redox activity. Nevertheless, the fast capacity decay and poor rate capability hinder their application in sodium-ion batteries (SIBs). In this work, a facile template method, using Prussian blue analogue as the precursor, was employed to fabricate the double-shelled hollow structure with nitrogen (N)-doped carbon layer coated on the outer shell of NiS-FeS hollow nanocubes (NiS-FeS@NC). Specifically, the NiS-FeS@NC composite provides a glorious reversible capacity (395.8 mAh g−1 after 200 cycles at 1.0 A g−1) and superior rate capability (322.2 mAh g−1 at 5.0 A g−1) when employed as electrode material for SIBs. In addition, the designed NiS-FeS@NC= |Na3V2(PO4)2 @C full battery can maintain an excellent reversible capacity (68.7 mAh g−1 after 200 cycles at 1.0 A g−1) as well as robust cycling stability. The improvement of the sodium storage performance can be mainly attributed to the synergistic coupling of the N-doped carbon coating layer and the hollow nanostructure, which can effectively maintain the integrity of the nanocube structure and accelerate the transportation of electrons/ions, thus contributing to improving the electrochemical sodium storage performance. Importantly, the unique modification strategy reported in this paper, combining with the mechanism and kinetics revealing, is expected to provide insights for facilitating high-performance electrodes for SIBs.