BackgroundAlpha-synuclein can be detected in skin biopsies of individuals with synucleinopathies. However, quantitative data of total and phosphorylated Serine 129 (pS129) alpha-synuclein in skin biopsies are scarce.ObjectiveWe aimed to investigate the biomarker potential of quantitative total and pS129 alpha-synuclein measurements in skin biopsies from people with synucleinopathies and controls.MethodsWe developed and validated AlphaLISA™ immunoassays to determine total and pS129 alpha-synuclein concentrations. Postmortem skin biopsies of Parkinson's disease (PD: n = 18), Dementia with Lewy bodies (DLB: n = 3), Multiple System Atrophy (MSA: n = 5) and control (n = 5) subjects were collected at the cervical vertebra C7. Brain tissues (middle temporal gyrus and substantia nigra) were collected from these same cases. In addition, skin biopsies of controls (n = 20) and PD cases (n = 40) were obtained from the ProPark cohort.ResultsTotal and pSer129 alpha-synuclein could be robustly detected and quantified in all skin samples. We observed a trend towards increased total (+58%, p = 0.055) and pS129 (+131%, p = 0.060) alpha-synuclein skin concentrations in synucleinopathy cases compared to controls. We found no correlations between pS129 alpha-synuclein concentrations in paired brain and skin tissues from the same donors. pS129 alpha-synuclein concentrations were similar for clinical PD cases and controls and there was no correlation with motor symptom severity (UPDRS-III).ConclusionsThese findings highlight that total and pS129 alpha-synuclein can be biochemically quantified in skin biopsies, but warrant further validation and investigation to asses its potential as a diagnostic biomarker in clinical cohorts.

Attention bias towards social threat has been linked to loneliness and anxiety, though findings are mixed and concerns about measurement reliability persist. This study examined whether state and trait loneliness, along with personality, self-esteem, social anxiety, and life satisfaction, are associated with attention bias towards social threat images (indicating rejection or exclusion) in young adults (N = 241). AI-generated images were used to enhance control over stimulus content and category distinctions. Participants completed an eye-tracking free-viewing task comprising 40 image matrices (four images per matrix, displayed for 6000 ms). We then computed attention bias (dwell time percentage, total fixation duration percentage, and fixation count percentage) and initial orientation of attention (first fixation percentage). The attention bias measures showed adequate-to-good internal consistency (α = 0.61–0.86). No significant associations emerged between loneliness and attention to socially threatening stimuli, suggesting that heightened vigilance to social threat may not be a feature of loneliness in non-clinical young adults. However, it was found that females exhibited greater attention to social positive images, and baseline pupil diameter was associated with social anxiety. Future research should assess whether loneliness-specific attention bias is a replicable phenomenon, ideally by using an extreme-sampling approach with very lonely individuals.

This study investigated human performance in identifying AI-generated images. In a speeded forced-choice task, 255 participants viewed paired images (one real, one AI-generated by Midjourney) of standard or futuristic cars and buildings and had to identify the AI-generated one, while eye movements were recorded using an eye-tracker. Results revealed a powerful “futurism-as-artificiality” heuristic. Specifically, participants performed poorly (55% correct) when an AI-generated standard image was paired with a real futuristic image. Conversely, accuracy was high (91% correct) when the AI-generated futuristic image was paired with a real standard image. Participants’ gaze landed first on the AI-generated image more often when it depicted a futuristic design than when it depicted a standard one. The demonstrated heuristic presents a double-edged sword for information veracity: it may lead to the uncritical acceptance of AI-generated misinformation that appears conventional, while simultaneously causing real forward-thinking designs to be dismissed as fake.