Solution Synthesis and Light-Emitting Applications of One-Dimensional Lead-Free Cerium(III) Metal Halides.

Combining rare earth elements with the halide perovskite structure offers valuable insights into designing nonlead (Pb) luminescent materials. However, most of these compositions tend to form zero-dimensional (0D) networks of metal-halide polyhedra, with higher-dimensional (1D, 2D, and 3D) structures receiving relatively less exploration. Herein, we present synthesis and optical properties of Cs3CeCl6·3H2O, characterized by its unique 1D crystal structure. The conduction band minimum of Cs3CeCl6·3H2O becomes less localized as a result of the increased structural dimension, making it possible for the materials to achieve an efficient electrical injection. For both Cs3CeCl6·3H2O single crystals and nanocrystals, we also observed remarkable luminescence with near-unity photoluminescence quantum yield and exceptional phase stability. Cs3CeCl6·3H2O single crystals demonstrate an X-ray scintillation light yield of 31900 photons/MeV, higher than that of commercial LuAG:Ce (22000 photons/MeV); electrically driven light-emitting diodes fabricated with Cs3CeCl6·3H2O nanocrystals yield the characteristic emission of Ce3+, indicating their potential use in next-generation violet-light-emitting devices.

Machine learning for the prediction of delirium in elderly intensive care unit patients.

PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage. METHODS: Patients admitted to ICU for at least 24 h and using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (76,943 ICU stays from 2008 to 2019) were considered. Patients with a positive delirium test in the first 24 h and under 65 years of age were excluded. Two prediction models, machine learning extreme gradient boosting (XGBoost) and logistic regression (LR) model, were developed and validated to predict the onset of delirium. RESULTS: Of the 18,760 patients included in the analysis, 3463(18.5%) were delirium positive. A total of 22 significant predictors were selected by LASSO regression. The XGBoost model demonstrated superior performance over the LR model, with the Area Under the Receiver Operating Characteristic (AUC) values of 0.853 (95% confidence interval [CI] 0.846-0.861) and 0.831 (95% CI 0.815-0.847) in the training and testing datasets, respectively. Moreover, the XGBoost model outperformed the LR model in both calibration and clinical utility. The top five predictors associated with the onset of delirium were sequential organ failure assessment (SOFA), infection, minimum platelets, maximum systolic blood pressure (SBP), and maximum temperature. CONCLUSION: The XGBoost model demonstrated good predictive performance for delirium among elderly ICU patients, thus assisting clinicians in identifying high-risk patients at the early stage and implementing targeted interventions to improve outcome.

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis.

Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection. COVID-19 datasets (GSE164805 and GSE180594) and aging dataset (GSE69832) were obtained from the Gene Expression Omnibus database. The combined different expression genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Diseases Ontology functional enrichment analysis, Gene Set Enrichment Analysis, machine learning, and immune cell infiltration analysis. GO and KEGG enrichment analyses revealed that the eight DEGs (IL23A, PTGER4, PLCB1, IL1B, CXCR1, C1QB, MX2, ALOX12) were mainly involved in inflammatory mediator regulation of TRP channels, coronavirus disease-COVID-19, and cytokine activity signaling pathways. Three-degree algorithm (LASSO, SVM-RFE, KNN) and correlation analysis showed that the five DEGs up-regulated the immune cells of macrophages M0/M1, memory B cells, gamma delta T cell, dendritic cell resting, and master cell resisting. Our study identified five hallmark genes that can serve as disease-characteristic genes and target immune cells infiltrated in severe COVID-19 patients among the elderly population, which may contribute to the study of pathogenesis and the evaluation of diagnosis and prognosis in aging patients infected with severe COVID-19.

Excitation Wavelength-Dependent Fluorescence of a Lanthanide Organic Metal Halide Cluster for Anti-Counterfeiting Applications.

The achievement of significant photoluminescence (PL) in lanthanide ions (Ln3+ ) has primarily relied on host sensitization, where energy is transferred from the excited host material to the Ln3+ ions. However, this luminous mechanism involves only one optical antenna, namely the host material, which limits the accessibility of excitation wavelength-dependent (Ex-De) PL. Consequently, the wider application of Ln3+ ions in light-emitting devices is hindered. In this study, we present an organic-inorganic compound, (DMA)4 LnCl7 (DMA+ =[CH3 NH2 CH3 ]+ , Ln3+ =Ce3+ , Tb3+ ), which serves as an independent host lattice material for efficient Ex-De emission by doping it with trivalent antimony (Sb3+ ). The pristine (DMA)4 LnCl7 compounds exhibit high luminescence, maintaining the characteristic sharp emission bands of Ln3+ and demonstrating a high PL quantum yield of 90-100 %. Upon Sb3+ doping, the compound exhibits noticeable Ex-De emission with switchable colors. Through a detailed spectral study, we observe that the prominent energy transfer process observed in traditional host-sensitized systems is absent in these materials. Instead, they exhibit two independent emission centers from Ln3+ and Sb3+ , each displaying distinct features in luminous color and radiative lifetime. These findings open up new possibilities for designing Ex-De emitters based on Ln3+ ions.

Tailoring multifunctional anions to inhibit methanol absorption on a CsPbBr3 quantum dot surface for highly efficient semi-transparent photovoltaics.

Methyl acetate (MeOAc) is the most used antisolvent in the preparation of perovskite quantum dot (QD) films. However, the hydrolysis of MeOAc results in acetic acid and methanol (MeOH), and the decomposition of the perovskite occurs more easily under acidic and polar conditions. Herein, we report a facile and universal anion modification strategy to inhibit MeOH absorption on a perovskite QD surface and improve the photovoltaic performance of perovskite QD solar cells, which is implemented by incorporating a series of guanidinium salts containing different anions (guanidinium bromide (GuaBr), guanidinium thiocyanate (GuaSCN), and guanidinium acetate (GuaAc)). All anions play a positive role in inhibiting the absorption of MeOH on the QD surface, facilitating charge transfer between perovskite QDs and passivating the defects. Moreover, the regulation of surface chemistry can be optimized by rational tailoring of different anion species. The GuaAc-based devices deliver a PCE of 7.04%, which is the highest value among inorganic CsPbBr3 QD solar cells. More importantly, the CsPbBr3 QD solar cells exhibit high transparency over the entire visible spectrum region, indicating their promising application in solar windows.