Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD

COVID-19 mRNA vaccine-mediated antibodies in human breast milk and their association with breast milk microbiota composition

Abstract Newborns can acquire immunological protection to SARS-CoV-2 through vaccine-conferred antibodies in human breast milk. However, there are some concerns around lactating mothers with regards to potential short- and long-term adverse events and vaccine-induced changes to their breast milk microbiome composition, which helps shape the early-life microbiome. Thus, we sought to explore if SARS-CoV-2 mRNA vaccine could change breast milk microbiota and how the changes impact the levels of antibodies in breast milk. We recruited 49 lactating mothers from Hong Kong who received two doses of BNT162b2 vaccine between June 2021 and August 2021. Breast milk samples were self-collected by participants pre-vaccination, one week post-first dose, one week post-second dose, and one month post-second dose. The levels of SARS-CoV-2 spike-specific IgA and IgG in breast milk peaked at one week post-second dose. Subsequently, the levels of both antibodies rapidly waned in breast milk, with IgA levels returning to baseline levels one month post-second dose. The richness and composition of human breast milk microbiota changed dynamically throughout the vaccination regimen, but the abundances of beneficial microbes such as Bifidobacterium species did not significantly change after vaccination. Additionally, we found that baseline breast milk bacterial composition can predict spike-specific IgA levels at one week post-second dose (Area Under Curve: 0.72, 95% confidence interval: 0.58–0.85). Taken together, our results identified specific breast milk microbiota markers associated with high levels of IgA in the breast milk following BNT162b2 vaccine. Furthermore, in lactating mothers, BNT162b2 vaccines did not significantly reduce probiotic species in breast milk