Both plasma tau phosphorylated at threonine-181 (pTau181) and tau positron emission tomography (tau-PET) show potential for detecting Alzheimer's disease (AD) pathology and predicting clinical progression. In this study, we performed a head-to-head comparison between plasma pTau181 and tau-PET along the AD continuum. Methods:We included participants from the Amsterdam Dementia Cohort that underwent 18 F-flortaucipir (tau)-PET and had a plasma sample biobanked within 12-months from tau-PET.Fifty subjective cognitive decline (SCD) participants (31 Aβ-negative and 19 Aβ-positive) and 60Aβ-positive participants with mild cognitive impairment or dementia due to AD (MCI/AD) were included. A subset had 2-year longitudinal plasma pTau181 and tau-PET available (n=40).Longitudinal neuropsychological test data covering 3.22.7 years from both before and after tau-PET was available. Plasma pTau181 and tau-PET were compared in their accuracies in discriminating between cognitive stage (MCI/AD versus SCD) and preclinical Aβ-status (SCD Aβpositive versus SCD Aβ-negative), their associations with cross-sectional and longitudinal neuropsychological test performance, and their longitudinal changes over time.Results: When discriminating between preclinical Aβ-status, the area under the curve (AUC) for plasma pTau181 (AUC: 0.83) and tau-PET (AUCs: entorhinal: 0.87; temporal: 0.85; neocortical: 0.67) were equally high (all DeLong's p>0.05), but tau-PET outperformed plasma pTau181 in discriminating MCI/AD from SCD (AUC plasma pTau181: 0.74; AUCs tau-PET: entorhinal: 0.89; temporal: 0.92; neocortical: 0.89) (all p<0.01). Overall, tau-PET showed stronger associations with cognitive decline and was associated with a wider variety of cognitive tests compared to plasma pTau181 (β-range plasma pTau181: -0.02>β<-0.12; β-range tau-PET: -0.01>β<-0.22). Both plasma pTau181 and tau-PET increased more steeply over time in MCI/AD compared to SCD (p<0.05), but only tau-PET annual changes associated with cognitive decline. Conclusions:Our results suggest that plasma pTau181 and tau-PET perform equally well in identifying Aβ pathology, but tau-PET better monitors disease staging and clinical progression.

Microfluidics has become a popular method for constructing nanosystems in recent years, but it can also be used to coat other materials with polymeric layers. The polymeric coating may serve as a diffusion barrier against hydrophilic compounds, a responsive layer for controlled release, or a functional layer introduced to a nanocomposite for achieving the desired surface chemistry. In this study, mesoporous silica nanoparticles (MSNs) with enlarged pores were synthesized to achieve high protein loading combined with high protein retention within the MSN system with the aid of a microfluidic coating. Thus, MSNs were first coated with a cationic polyelectrolyte, poly (diallyldimethylammonium chloride) (PDDMA), and to potentially further control the protein release, a second coating of a pH-sensitive polymer (spermine-modified acetylated dextran, SpAcDEX) was deposited by a designed microfluidic device. The protective PDDMA layer was first formed under aqueous conditions, whereby the bioactivity of the protein could be maintained. The second coating polymer, SpAcDEX, was preferred to provide pH-sensitive protein release in the intracellular environment. The optimized formulation was effectively taken up by the cells along with the loaded protein cargo. This proof-of-concept study thus demonstrated that the use of microfluidic technologies for the design of protein delivery systems has great potential in terms of creating multicomponent systems and preserving protein stability.

Background and objectives:Excessive activation of certain lipid mediator (LM) pathways play a role in the complex pathogenesis of multiple sclerosis (MS). However, the relation between bioactive LMs and different aspects of CNS-related pathophysiological processes remains largely unknown. Therefore, we here assessed the association of bioactive LMs belonging to the ω-3 / ω-6 lipid classes with clinical, biochemical (serum neurofilament light (sNfL) and serum glial fibrillary acidic protein (sGFAP)) parameters and MRI-based brain volumes in patients with MS (PwMS) and healthy controls (HC).Methods:A targeted high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) approach was used on plasma samples of PwMS and HC of the Project Y cohort, a cross-sectional population based cohort that contains PwMS all born in 1966 in the Netherlands and age-matched HCs. LMs were compared between PwMS and HC and were correlated with levels of sNfL, sGFAP, disability (EDSS) and brain volumes. Finally, significant correlates were included in a backward multivariate regression model to identify which LMs best related to disability.Results:The study sample consisted of 170 patients with relapsing remitting MS (RRMS), 115 patients with progressive MS (PMS) and 125 HC. LM profiles of patients with PMS significantly differed from RRMS and HC, in particular, patients with PMS showed elevated levels of several arachidonic acid (AA) derivatives. In particular 15-HETE (r = 0.24,p< 0.001), positively correlated (average r = 0.2,p< 0.05) with clinical and biochemical parameters such as EDSS and sNfL. In addition, higher 15-HETE levels were related to lower total brain (r = -0.24,p= 0.04) and deep gray matter volumes (r = -0.27,p= 0.02) in patients with PMS and higher lesion volume (r = 0.15,p =0.03) in all PwMS.Discussion:In PwMS of the same birth year, we show that ω-3 and -6 LMs are associated with disability, biochemical (sNfL, GFAP) and MRI measures. Furthermore, our findings indicate that particularly in patients with PMS, elevated levels of specific products of the AA pathway, such as 15-HETE, associate with neurodegenerative processes. Our findings highlight the potential relevance of ω-6 LMs in the pathogenesis of MS.

BackgroundSeveral blood‐based biomarkers for Alzheimer’s disease (AD) can now be reliably measured. It is not yet clear how those markers should be interpreted in a real‐world clinical setting, where a heterogeneous group of patients presents in different syndromal stages and with different symptoms. We aim to establish a ready‐to‐implement blood‐based biomarker panel including Abeta1‐42/1‐40, P‐tau181, GFAP and NfL with cutoffs for clinical application among subjective cognitive decline (SCD), mild cognitive impairment (MCI), AD‐dementia, frontotemporal dementia (FTD) and dementia with Lewy bodies (DLB).MethodWe included n=1132 individuals of the Amsterdam Dementia Cohort (table 1) with known baseline amyloid‐beta (Aβ) status, who had SCD (n=237 Aβ‐, n=66 Aβ+), MCI (n=108 Aβ‐, n=151 Aβ+), AD‐dementia (n=297, all Aβ+), FTD (n=162 of whom n=121 Aβ) or DLB (n=111 of whom n=53 Aβ‐). Plasma markers were measured with Simoa (neurology 4‐plex E, P‐tau181 V2). We applied ROC analysis and subsequently LASSO regression to identify panels among the plasma markers.ResultROC analysis (table 2, figure 1) showed that among SCD and MCI, AUCs of the individual plasma markers to predict Aβ status were 0.738–0.799 (lowest for NfL, highest for P‐tau181). LASSO regression (table 2, figure 1) selected Abeta1‐42/1‐40, P‐tau181 and GFAP, but not NfL as the optimal panel (AUC=0.846, 95%CI: 0.812–0.880). Stratifying for SCD or MCI stage resulted in largely similar findings (not shown). When discriminating AD from FTD, AUCs of the individual plasma markers were 0.643–0.820 (lowest for Abeta1‐42/1‐40, highest for P‐tau). LASSO regression selected P‐tau181, GFAP and NfL as the optimal panel (AUC=0.889, 95%CI: 0.850–0.927). For AD versus DLB, AUCs were 0.514–0.741 (lowest for NfL, highest for P‐tau181). LASSO regression selected only P‐tau181 (AUC=0.741, 95%CI: 0.683–0.800). Plasma marker cutoffs at Youden’s indices differed depending on the diagnostic groups, with higher cutoffs for P‐tau181, GFAP and NfL in AD versus FTD or DLB analyses, compared to the SCD and MCI analyses (table 2).ConclusionWe showed that depending on the clinical question at hand, the four biomarkers add valuable information with their own cutoffs. Decision trees might lead to ready‐to‐apply cutoffs for daily clinical practice.