Subcomponent self-assembly of circular helical Dy6_{6}(L)6_{6} and bipyramid Dy12_{12}(L)8_{8} architectures directed via second-order template effects

In situ metal-templated (hydrazone) condensation also called subcomponent self-assembly of 4,6-dihydrazino-pyrimidine, o-vanillin and dysprosium ions resulted in the formation of discrete hexa- or dodecanuclear metallosupramolecular Dy$_{6}$(L)$_{6}$ or Dy$_{12}$(L)$_{8}$ aggregates resulting from second-order template effects of the base and the lanthanide counterions used in these processes. XRD analysis revealed unique circular helical or tetragonal bipyramid architectures in which the bis(hydrazone) ligand L adopts different conformations and shows remarkable differences in its mode of metal coordination. While a molecule of trimethylamine acts as a secondary template that fills the void of the Dy$_{6}$(L)$_{6}$ assembly, sodium ions take on this role for the formation of heterobimetallic Dy$_{12}$(L)$_{8}$ by occupying vacant coordination sites, thus demonstrating that these processes can be steered in different directions upon subtle changes of reaction conditions. Furthermore, Dy$_{6}$(L)$_{6}$ shows an interesting spin-relaxation energy barrier of 435 K, which is amongst the largest values within multinuclear lanthanide single-molecular magnets

Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. However, existing studies have used general deep computer vision methods, which present challenges in providing interpretable results and leveraging clinical prior knowledge. To address these challenges, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model's decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance.Comment: Accepted by MICCAI202

Superconductivity in trilayer nickelate La4Ni3O10 under pressure

Nickelates gained a great deal of attention due to their similar crystal and electronic structures of cuprates over the past few decades. Recently, superconductivity with transition temperature exceeding liquid-nitrogen temperature is discovered in La3Ni2O7, which belong to the Ruddlesden-Popper (RP) phases Lan+1NinO3n+1 with n = 2. In this work, we go further and find pressure-induced superconductivity in another RP phase La4Ni3O10 (n = 3) single crystals. Our angle-resolved photoemission spectroscopy (ARPES) experiment suggest that the electronic structure of La4Ni3O10 is very similar to that of La3Ni2O7. We find that the density-wave like anomaly in resistivity is progressively suppressed with increasing pressure. A typical phase diagram is obtained with the maximum Tc of 21 Kelvin. Our study sheds light on the exploration of unconventional superconductivity in nickelates.Comment: 16 pages, 5 figure

3D VESSEL RECONSTRUCTION IN OCT-ANGIOGRAPHY VIA DEPTH MAP ESTIMATION

Optical Coherence Tomography Angiography (OCTA) has been increasingly used in the management of eye and systemic diseases in recent years. Manual or automatic analysis of blood vessel in 2D OCTA images (en face angiograms) is commonly used in clinical practice, however it may lose rich 3D spatial distribution information of blood vessels or capillaries that are useful for clinical decision-making. In this paper, we introduce a novel 3D vessel reconstruction framework based on the estimation of vessel depth maps from OCTA images. First, we design a network with structural constraints to predict the depth of blood vessels in OCTA images. In order to promote the accuracy of the predicted depth map at both the overall structure- and pixel- level, we combine MSE and SSIM loss as the training loss function. Finally, the 3D vessel reconstruction is achieved by utilizing the estimated depth map and 2D vessel segmentation results. Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysi

Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study

BackgroundShort-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH.MethodsThis study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP).ResultsA total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77–0.86), high accuracy for 0.74 (95% CI: 0.72–0.76), sensitivity 0.78 (95% CI: 0.69–0.87), and specificity 0.74 (95% CI: 0.72–0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results.ConclusionsThis study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH