Knowledge of Alzheimer's disease and associated factors among adults in Zhuhai, China: a cross-sectional analysis.

BACKGROUND: This study aimed to assess the public knowledge regarding Alzheimer's Disease (AD) in Zhuhai, China, focusing on identifying knowledge gaps and the influence of demographic and health factors. METHODS: A cross-sectional study was conducted in Zhuhai, China, from October to November 2022. A total of 1986 residents from 18 communities were selected employing stratified multi-stage equi-proportional sampling. Questionnaires covering general information and the Alzheimer's Disease Knowledge Scale (ADKS) were investigated face-to-face. Ordinal multiclass logistic regression was applied to assess the relationship between AD awareness and demographic and health characteristics. RESULTS: The average ADKS score was 18.5 (SD = 3.36) in Zhuhai. The lowest awareness rates were observed in the "Symptoms" and "Caregiving" subdomains of ADKS, with rates of 51.01% and 43.78%, respectively. The correct rates for the 30 ADKS questions ranged from 16.62 to 92.6%, showing a bimodal pattern with clusters around 80% and 20%. Women (OR = 1.203, 95% CI: 1.009-1.435), individuals aged 60 years or older (OR = 2.073, 95% CI: 1.467-2.932), those living in urban areas (OR = 1.361, 95% CI: 1.117-1.662), higher average monthly household income per capita (OR = 1.641, 95% CI: 1.297-2.082), and without any neurological or mental disorders (OR = 1.810, 95% CI: 1.323-2.478) were more likely to have higher levels of awareness about Alzheimer's disease. CONCLUSIONS: Adults in Zhuhai show a limited knowledge of AD, particularly in the 'Symptoms' and 'Caregiving' subdomains. Upcoming health campaigns must focus on bridging the knowledge gaps in different subdomains of AD, especially among subgroups with lower awareness, as identified in our study.

S-Trimer, a COVID-19 subunit vaccine candidate, induces protective immunity in nonhuman primates.

SARS-CoV-2 is the underlying cause for the COVID-19 pandemic. Like most enveloped RNA viruses, SARS-CoV-2 uses a homotrimeric surface antigen to gain entry into host cells. Here we describe S-Trimer, a native-like trimeric subunit vaccine candidate for COVID-19 based on Trimer-Tag technology. Immunization of S-Trimer with either AS03 (oil-in-water emulsion) or CpG 1018 (TLR9 agonist) plus alum adjuvants induced high-level of neutralizing antibodies and Th1-biased cellular immune responses in animal models. Moreover, rhesus macaques immunized with adjuvanted S-Trimer were protected from SARS-CoV-2 challenge compared to vehicle controls, based on clinical observations and reduction of viral loads in lungs. Trimer-Tag may be an important platform technology for scalable production and rapid development of safe and effective subunit vaccines against current and future emerging RNA viruses.

Long-term exposure to urban air pollution and lung cancer mortality: A 12-year cohort study in Northern China.

Cohort evidence that links long-term exposures to air pollution and mortality comes largely from the United States and European countries. We investigated the relationship between long-term exposures to particulate matter <10µm in diameter (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2) and mortality of lung cancer in Northern China. A cohort of 39,054 participants were followed during 1998-2009. Annual average concentrations for PM10, NO2, and SO2 were determined based on data collected from central monitoring stations. Lung cancer deaths (n=140) were obtained from death certificates, and hazard ratios (HRs) were estimated using Cox proportional hazards models, adjusting for age, gender, BMI, education, marital status, smoking status, passive smoking, occupation, alcohol consumption, etc. Each 10mg/m(3) increase in PM10 concentrations was associated with a 3.4%-6.0% increase in lung cancer mortality in the time-varying exposure model and a 4.0%-13.6% increase in the baseline exposure model. In multi-pollutant models, the magnitude of associations was attenuated, most strongly for PM10. The association was different in men and women, also varying across age categories and different smoking status. Substantial differences exist in the risk estimates for participants based on assignment method for air pollution exposure.

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases.

High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn.

Learning the drug target-likeness of a protein.

Current drug discovery and development approaches rely extensively on the identification and validation of appropriate targets; for example, those with marketable and robust therapeutics. Wide-ranging efforts have been directed at this problem and various approaches have been developed to identify disease-associated genes as candidates. In this work, we show with statistical significance that successful drug targets, in addition to their linkage to disease, share common characteristics that are disease-independent. For example, marked differences in functional category, tissue specificity, and sequence variability are observed between known targets and average proteins. These results lead to an interesting hypothesis: potentially good drug targets shall have some desired properties, which we refer to as "drug target-likeness" that are beyond their disease-associations. Because of the limited availability of comprehensive protein characteristics data, we tried to learn the drug target-likeness property at the sequence level. Results show that a support vector machine model is able to accurately distinguish targets from nontargets entirely with sequence features. It is our hope that these encouraging results will invite future systematic proteomic scale experiments to gather necessary protein characteristics data for the accurate and predictive definition of "drug target-likeness", providing a new perspective toward understanding and pursuing effective therapeutics.