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IntroductionCoronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term.MethodsThis article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions.ResultsSuccessful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe.DiscussionThe Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection.Key pointsThe following review describes what is known so far in terms of molecular and epidemiological links among COVID-19, the brain, neurological symptoms, and AD and related dementias (ADRD)The primary objective of this large-scale collaboration is to clarify the pathogenesis of ADRD and to advance our understanding of the impact of a neurotropic virus on the long-term risk of cognitive decline and other CNS sequelae. No available evidence supports the notion that cognitive impairment after SARS-CoV-2 infection is a form of dementia (ADRD or otherwise). The longitudinal methodologies espoused by the consortium are intended to provide data to answer this question as clearly as possible controlling for possible confounders. Our specific hypothesis is that SARS-CoV-2 triggers ADRD-like pathology following the extended olfactory cortical network (EOCN) in older individuals with specific genetic susceptibility.The proposed harmonization strategies and flexible study designs offer the possibility to include large samples of under-represented racial and ethnic groups, creating a rich set of harmonized cohorts for future studies of the pathophysiology, determinants, long-term consequences, and trends in cognitive aging, ADRD, and vascular disease.We provide a framework for current and future studies to be carried out within the Consortium. and offers a "green paper" to the research community with a very broad, global base of support, on tools suitable for low- and middle-income countries aimed to compare and combine future longitudinal data on the topic.The Consortium proposes a combination of design and statistical methods as a means of approaching causal inference of the COVID-19 neuropsychiatric sequelae. We expect that deep phenotyping of neuropsychiatric sequelae may provide a series of candidate syndromes with phenomenological and biological characterization that can be further explored. By generating high-quality harmonized data across sites we aim to capture both descriptive and, where possible, causal associations.

The consciousness and behavior of students in the Ideological and Political Education curriculum generate massive amounts of data information, how to allow educators to quickly obtain information from the massive amount of text data is very important to improve the energy efficiency of classroom education. SVM technology is employed in this paper for data mining and text classification. Firstly, using the web crawler method, the text of messages related to the Ideological and Political Science course posted by students on social networks is collected. The short text was preprocessed before performing sentiment analysis, which mainly included interaction information filtering, word segmentation, and lexical labeling. Then, the SVM model suitable for students’ ideology analysis is constructed, using the Gauss radial basis kernel function to accurately depict the distribution structure of the data, and the L1-SVM model with more stable computational performance is also proposed. The extension method of the classification algorithm in the real number domain is summarized at the end. This algorithm’s accuracy is 78%, and its F1 value is 80%, which is higher than the other three algorithms. DAG-SVM and recall are both optimized to a lesser extent. Overall, the classification efficiency of the algorithm in this paper has been improved. The positive effect of this paper’s algorithm on improving the effectiveness of Ideological and Political Education can be seen in the significant increase in the learning interest of the experimental class.

Background The World Health Organization (WHO) has reframed health and healthcare for older people around achieving the goal of healthy ageing. The recent WHO Integrated Care for Older People (ICOPE) guidelines focus on maintaining intrinsic capacity, i.e., addressing declines in neuromusculoskeletal, vitality, sensory, cognitive, psychological, and continence domains, aiming to prevent or delay the onset of dependence. The target group with 1 or more declines in intrinsic capacity (DICs) is broad, and implementation may be challenging in less-resourced settings. We aimed to inform planning by assessing intrinsic capacity prevalence, by characterising the target group, and by validating the general approach-testing hypotheses that DIC was consistently associated with higher risks of incident dependence and death. Methods and findings We conducted population-based cohort studies (baseline, 2003-2007) in urban sites in Cuba, Dominican Republic, Puerto Rico, and Venezuela, and rural and urban sites in Peru, Mexico, India, and China. Door-knocking identified eligible participants, aged 65 years and over and normally resident in each geographically defined catchment area. Sociodemographic, behaviour and lifestyle, health, and healthcare utilisation and cost questionnaires, and physical assessments were administered to all participants, with incident dependence and mortality ascertained 3 to 5 years later (2008-2010). In 12 sites in 8 countries, 17,031 participants were surveyed at baseline. Overall mean age was 74.2 years, range of means by site 71.3-76.3 years; 62.4% were female, range 53.4%-67.3%. At baseline, only 30% retained full capacity across all domains. The proportion retaining capacity fell sharply with increasing age, and declines affecting multiple domains were more common. Poverty, morbidity (particularly dementia, depression, and stroke), and disability were concentrated among those with DIC, although only 10% were frail, and a further 9% had needs for care. Hypertension and lifestyle risk factors for chronic disease, and healthcare utilisation and costs, were more evenly distributed in the population. In total, 15,901 participants were included in the mortality cohort (2,602 deaths/53,911 person-years of follow-up), and 12,939 participants in the dependence cohort (1,896 incident cases/38,320 person-years). One or more DICs strongly and independently predicted incident dependence (pooled adjusted subhazard ratio 1.91, 95% CI 1.69-2.17) and death (pooled adjusted hazard ratio 1.66, 95% CI 1.49-1.85). Relative risks were higher for those who were frail, but were also substantially elevated for the much larger sub-groups yet to become frail. Mortality was mainly concentrated in the frail and dependent sub-groups. The main limitations were potential for DIC exposure misclassification and attrition bias. Conclusions In this study we observed a high prevalence of DICs, particularly in older age groups. Those affected had substantially increased risks of dependence and death. Most needs for care arose in those with DIC yet to become frail. Our findings provide some support for the strategy of optimising intrinsic capacity in pursuit of healthy ageing. Implementation at scale requires community-based screening and assessment, and a stepped-care intervention approach, with redefined roles for community healthcare workers and efforts to engage, train, and support them in these tasks. ICOPE might be usefully integrated into community programmes for detecting and case managing chronic diseases including hypertension and diabetes., Author(s): Martin J. Prince 1,2,*, Daisy Acosta 3, Mariella Guerra 4,5, Yueqin Huang 6, K. S. Jacob 7, Ivonne Z. Jimenez-Velazquez 8, A. T. Jotheeswaran 9, Juan J. Llibre Rodriguez [...]