Cognitive decline among older adults with heart diseases before and during the COVID-19 pandemic: A longitudinal cohort study

BackgroundLittle is known about the impact induced by the COVID-19 pandemic on the cognitive function of older adults with heart diseases. This study aimed to examine whether older adults with heart diseases suffered larger cognitive deterioration during the COVID-19 pandemic.MethodsThis study leveraged longitudinal data from the Health and Retirement Study (HRS), a nationally representative U.S. aging cohort with objective cognitive assessments measured before and during the pandemic. The interval from HRS waves 13 to 14 (April 2016 to June 2019) was defined as the pre-pandemic period to control the pre-existed cognitive difference between participants with and without heart diseases, and the interval from waves 14 to 15 (June 2019 to June 2021) was defined as the pandemic period. The HRS wave 14 survey was considered the baseline. The heart disease status was defined by a self-reported diagnosis. Linear mixed models were performed to evaluate and compare the cognitive differences during different periods.ResultsA total of 9,304 participants (women: 5,655, 60.8%; mean age: 65.8 ± 10.8 years) were included, and 2,119 (22.8%) had heart diseases. During the pre-pandemic period, there was no significant difference (−0.03, 95% CI: −0.22 to 0.15, P = 0.716) in the changes in global cognitive scores between participants with and without heart disease. During the pandemic period, a larger decreased change in the global cognitive score was observed in the heart disease group compared with the non-heart disease group (−0.37, 95% CI: −0.55 to −0.19, P < 0.001). An enlarged difference in global cognitive score was observed during the pandemic period (−0.33, 95% CI: −0.65 to −0.02, P = 0.036).ConclusionThe findings demonstrated that the population with heart diseases suffered more cognitive decline related to the pandemic, underscoring the necessity to provide immediate cognitive monitoring and interventions for the population with heart diseases

A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability

To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering

Association Between Onset Age of Coronary Heart Disease and Incident Dementia: A Prospective Cohort Study

Background The association of age at coronary heart disease (CHD) onset with incident dementia remains unexplored. This study aimed to examine whether younger onset age of CHD is associated with a higher risk of incident dementia. Methods and Results Data were obtained from the UK Biobank. Information on the diagnosis of CHD and dementia was collected at baseline and follow‐ups. Propensity score matching method and Cox proportional hazards models were used to evaluate the association between different ages at CHD onset and incident dementia. A total of 432 667 adults (mean±SD age, 56.9±8.1 years) were included, of whom 11.7% had CHD. Compared with participants without CHD, participants with CHD exhibited higher risks of developing all‐cause dementia, Alzheimer's disease, and vascular dementia. More importantly, younger age at CHD onset (per 10‐year decrease) was significantly associated with elevated risks of all‐cause dementia (hazard ratio [HR], 1.25 [95% CI, 1.20–1.30]; P<0.001), Alzheimer's disease (HR, 1.29 [95% CI, 1.20–1.38]; P<0.001), and vascular dementia (HR, 1.22 [95% CI, 1.13–1.31]; P<0.001). After propensity score matching, patients with CHD had significantly higher risks of all‐cause dementia, Alzheimer's disease, and vascular dementia than matched controls among all onset age groups, and the HRs gradually elevated with decreasing age at CHD onset. Conclusions Younger onset age of CHD is associated with higher risks of incident all‐cause dementia, Alzheimer's disease, and vascular dementia, underscoring the necessity to pay attention to the neurocognitive status of individuals diagnosed with CHD at younger age to conduct timely interventions to attenuate subsequent risk of incident dementia

Ligand-Directed Regioselectivity in Amine–Imine Nickel-Catalyzed 1‑Hexene Polymerization

1-Hexene polymerizations were carried out with amine–imine nickel complexes [(ArNC­(R¹)–(R²R³)­CNHAr)­NiBr₂, <b>1a</b>, R¹ = R² = R³ = Me, Ar = 2,6-(iPr)₂C₆H₃; <b>1b</b>, R¹ = R² = R³ = Me, Ar = 2,6-(Me)₂C₆H₃; <b>2a</b>, R¹ = Me, R² = R³ = H, Ar = 2,6-(iPr)₂C₆H₃; <b>3a</b>, R¹ = Me, R² = <i>t</i>Bu, R³ = H, Ar = 2,6-(iPr)₂C₆H₃] in the presence of MMAO or Et₂AlCl. The ligand-directed regioselectivity involving insertion fashion and chain walking in amine–imine nickel-catalyzed 1-hexene polymerization is clearly observed. Catalyst <b>1a</b> with two methyl substituents on the bridging carbon can polymerize 1-hexene to afford semicrystalline “polyethylene” with long methylene sequence (−(CH₂)<i>_n</i>–, <i>n</i> = 40–74) via a combination of 90% selectivity of 2,1-insertion fashion and precise chain walking, whereas catalyst <b>3a</b> with a <i>tert</i>-butyl on the bridging carbon can polymerize 1-hexene in 80% selectivity of 1,2-insertion to produce amorphous polyolefin with predominant methyl branches through 2,6-enchainment