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Halide solid electrolytes (SEs) have attracted significant attention due to their competitive ionic conductivity and good electrochemical stability. Among typical halide SEs (chlorides, bromides, and iodides), substantial efforts have been dedicated to chlorides or bromides, with iodide SEs receiving less attention. Nevertheless, compared with chlorides or bromides, iodides have both a softer Li sublattice and lower reduction limit, which enable iodides to possess potentially high ionic conductivity and intrinsic anti‐reduction stability, respectively. Herein, we report a new series of iodide SEs: LixYI3+x (x=2, 3, 4, or 9). Through synchrotron X‐ray/neutron diffraction characterizations and theoretical calculations, we revealed that the LixYI3+x SEs belong to the high‐symmetry cubic structure, and can accommodate abundant vacancies. By manipulating the defects in the iodide structure, balanced Li‐ion concentration and generated vacancies enables an optimized ionic conductivity of 1.04 × 10−3 S cm−1 at 25 °C for Li4YI7. Additionally, the promising Li‐metal compatibility of Li4YI7 is demonstrated via electrochemical characterizations (particularly all‐solid‐state Li‐S batteries) combined with interface molecular dynamics simulations. Our study on iodide SEs provides deep insights into the relation between high‐symmetry halide structures and ionic conduction, which can inspire future efforts to revitalize halide SEs. [ABSTRACT FROM AUTHOR]

Aim: This study aims to assess the association between trajectories of depressive symptoms and the risk of dementia, and to compare the predictive ability of trajectories using multiple data points with depressive symptoms at a single data point. Methods: We included 5306 older adults from the Health and Retirement Study. We assessed depressive symptoms using the Center for Epidemiology Depression Scale (CES‐D), and identified its 8‐ year trajectories (2002–2010) using latent class trajectory modeling. We calculated hazard ratios (HR) using Cox proportional hazards models. The concordance index (C‐index) was used to compare the discriminative power of the models. Results: We identified two trajectories of depressive symptoms, characterized by maintaining low CES‐D scores, and moderate starting scores that steadily increased throughout the follow‐up period. During 40,199 person‐years, compared to the low trajectory, the increasing trajectory of depressive symptoms was associated with a higher risk of dementia (HR = 1.35; 95% CI: 1.09–1.67) (C‐index = 0.759). For every point increase in the degree of depressive symptoms (CES‐D scores) in 2010, the risk of dementia increased by 7% (95% CI: 1.03–1.12) (C‐index = 0.760). The presence of depressive symptoms (CES‐D scores ≥3) in 2010 was not associated with an increased risk of dementia (HR = 1.18; 95% CI: 0.98–1.43) (C‐index = 0.759). The C‐index values of cox models showed similar discriminative power. Conclusions: The increasing trajectory of depressive symptoms at multiple data points and the degree of depressive symptoms at a single data point were associated with an increased risk of subsequent dementia among older adults. [ABSTRACT FROM AUTHOR]

Seneca Valley virus (SVV) causes vesicular disease in pigs, posing a threat to global pork production. OPTN (optineurin) is a macroautophagy/autophagy receptor that restricts microbial propagation by targeting specific viral or bacterial proteins for degradation. OPTN is degraded and cleaved at glutamine 513 following SVV infection via the activity of viral 3C protease (3C[pro]), resulting in N-terminal and a C-terminal OPTN fragments. Moreover, OPTN interacts with VP1 and targets VP1 for degradation to inhibit viral replication. The N-terminal cleaved OPTN sustained its interaction with VP1, whereas the degradation capacity targeting VP1 decreased. The inhibitory effect of N-terminal OPTN against SVV infection was significantly reduced, C-terminal OPTN failed to inhibit viral replication, and degradation of VP1 was blocked. The knockdown of OPTN resulted in reduced TBK1 activation and phosphorylation of IRF3, whereas overexpression of OPTN led to increased TBK1-IRF3 signaling. Additionally, the N-terminal OPTN diminished the activation of the type I IFN (interferon) pathway. These results show that SVV 3C[pro] targets OPTN because its cleavage impairs its function in selective autophagy and type I IFN production, revealing a novel model in which the virus develops diverse strategies for evading host autophagic machinery and type I IFN response for survival. Abbreviations: Co-IP: co-immunoprecipitation; GFP-green fluorescent protein; hpi: hours post-infection; HRP: horseradish peroxidase; IFN: interferon; IFNB/IFN-β: interferon beta; IRF3: interferon regulatory factor 3; LIR: LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MOI: multiplicity of infection; OPTN: optineurin; PBS: phosphate-buffered saline; SVV: Seneca Valley virus; SQSTM1: sequestosome 1; TAX1BP1: Tax1 binding protein 1; TBK1: TANK binding kinase 1; TCID50: 50% tissue culture infectious doses; UBAN: ubiquitin binding in TNIP/ABIN (TNFAIP3/A20 and inhibitor of NFKB/NF-kB) and IKBKG/NEMO; UBD: ubiquitin-binding domain; ZnF: zinc finger. [ABSTRACT FROM AUTHOR]

Background: Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS. Purpose: To explore whether DLR from MRI can be used to identify pregnancies with PAS. Study Type: Retrospective. Population: 324 pregnant women (mean age, 33.3 years) suspected PAS (170 training and 72 validation from institution 1, 82 external validation from institution 2) with clinicopathologically proved PAS (206 PAS, 118 non‐PAS). Field Strength/Sequence: 3‐T, turbo spin‐echo T2‐weighted images. Assessment: The DLR features were extracted using the MedicalNet. An MRI‐based DLR model incorporating DLR signature, clinical model (different clinical characteristics between PAS and non‐PAS groups), and MRI morphologic model (radiologists' binary assessment for the PAS diagnosis) was developed. These models were constructed in the training dataset and then validated in the validation datasets. Statistical Tests: The Student t‐test or Mann–Whitney U, χ2 or Fisher exact test, Kappa, dice similarity coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator logistic regression, multivariate logistic regression, receiver operating characteristic (ROC) curve, DeLong test, net reclassification improvement (NRI) and integrated discrimination improvement (IDI), calibration curve with Hosmer–Lemeshow test, decision curve analysis (DCA). P < 0.05 indicated a significant difference. Results: The MRI‐based DLR model had a higher area under the curve than the clinical model in three datasets (0.880 vs. 0.741, 0.861 vs. 0.772, 0.852 vs. 0.675, respectively) or MRI morphologic model in training and independent validation datasets (0.880 vs. 0.760, 0.861, vs. 0.781, respectively). The NRI and IDI were 0.123 and 0.104, respectively. The Hosmer–Lemeshow test had nonsignificant statistics (P = 0.296 to 0.590). The DCA offered a net benefit at any threshold probability. Data Conclusion: An MRI‐based DLR model may show better performance in diagnosing PAS than a clinical or MRI morphologic model. Level of Evidence: 3 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]