Background This study aimed to investigate the associations of sarcopenia and its defining components with cognitive function in community-dwelling oldest old (over 80 years old) in China. Methods Sarcopenia was diagnosed by the 2019 Asian Working Group for Sarcopenia (AWGS) criteria. Cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA). Logistic and linear regression models were used to explore the associations of sarcopenia and its defining components with risk of mild cognitive impairment (MCI), and performance on multiple cognitive domains among 428 adults aged 80 years and older. Results The overall prevalence of sarcopenia was 35.5%, with 40.34% for men and 32.14% for women. The prevalence of MCI was higher among sarcopenic oldest old than non-sarcopenic oldest old (28.95% vs. 17.39%, p = 0.005). Multivariate logistic regression analyses showed that sarcopenia [odds ratio (OR) = 1.86, 95% confidence interval (CI): 1.04–3.33], low handgrip strength (HS) [OR = 2.33, 95% CI: 1.40–3.87] and slow gait speed (GS) [OR = 2.31, 95% CI: 1.13–4.72] were significantly and independently associated with risk of MCI. Multivariate linear regression analyses showed that low HS was associated with worse performance in global cognitive function, visuospatial and executive function, naming and delayed recall. Conclusions Sarcopenia, low HS and low GS was significantly associated with MCI in community-dwelling oldest old. The associations between sarcopenia and its defining components with different cognitive subdomains could be further explored in the future.

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Intermolecular forces constrain peptide conformation. However, the role of each intermolecular force in constraining peptide conformation remains poorly understood. In this work, we show that aromatic–aromatic interactions drive peptides into β‐sheets, and the hydrophobic effect determines the assembly speed of peptides. By using intermolecular forces to artificially control the assembly of β‐sheets, a multi‐modal analytical system was developed that allows five readouts and dual qualitative–quantitative analysis, and satisfies both point‐of‐care testing (POCT) and laboratory‐based testing. For Mycoplasma Pneumoniae diagnosis, this system eradicates misdiagnosis (from 30 % to 0 %) and broadens the linear range by three‐fold, both of which are critical for guiding therapy. This work not only illustrates exact roles of intermolecular forces in driving the formation of β‐sheets, but also provides a guideline for the construction of a multi‐modal analytical system for disease diagnosis.

Platelet function has been described by many laboratory assays, and PL-11 is a new point-of-care platelet function analyzer based on platelet count drop method, which counts platelet before and after the addition of agonists in the citrated whole blood samples. The present study sought to compare PL-11 with other three major more established assays, light transmission aggregometry (LTA), VerifyNow™ aspirin system and thromboelastography (TEG), for monitoring the short-term aspirin responses in healthy individuals. Ten healthy young men took 100 mg/d aspirin for 3-day treatment. Platelet function was measured via PL-11, LTA, VerifyNow and TEG, respectively. The blood samples were collected at baseline, 2 hour, 1 day during the aspirin treatment and 1 day, 5 ± 1 days, 8 ± 1 days after the aspirin withdrawal. Moreover, 90 additional healthy subjects were recruited to establish a reference range for PL-11. Platelet function of healthy subjects decreased significantly 2 hours after 100 mg/d aspirin intake and began to recover during 4-6 days after the aspirin withdrawal. Correlations between methods were PL-11 vs. LTA (r = 0.614, p < 0.01); PL-11 vs. VerifyNow (r = 0.829, p < 0.01); PL-11 vs. TEG (r = 0.697, p < 0.001). There was no significant bias between PL-11 and LTA at baseline (bias = 1.94%, p = 0.804) using Bland-Altman analysis, while the data of PL-11 were significantly higher than LTA (bias = 24.02%, p < 0.001) during the aspirin therapy. The reference range for PL-11 in healthy young individuals was from 66.8 to 90.5% (95%CI). When aspirin low-responsiveness was defined as LTA > 20%, the cut-off values for each method were, respectively: PL-11 > 50%, VerifyNow > 533 ARU, TEG > 60.2%. The results of different platelet function assays were uninterchangeable for monitoring aspirin response and correlations among them were also varied. Correlations among PL-11 and other three major assays suggested the ability of PL-11 to assess the treatment effects of aspirin. But a large cohort study is needed to confirm the cut-off value of aspirin response detected by PL-11.