Notes

Components impacting on (223)Ra treatments: medical knowledge right after 532 fertility cycles from just one organization.
ll-accepted, but the training should be challenging enough and adequately progress according to participants' capabilities to increase adherence. Recommendations for a larger on-line multidomain lifestyle training RCT are provided.
Providing multidomain lifestyle training through a web-platform is feasible and well-accepted, but the training should be challenging enough and adequately progress according to participants' capabilities to increase adherence. Recommendations for a larger on-line multidomain lifestyle training RCT are provided.
The Loewenstein Acevedo Scales of Semantic Interference and Learning (LASSI-L) is a novel and increasingly employed instrument that has outperformed widely used cognitive measures as an early correlate of elevated brain amyloid and neurodegeneration in prodromal Alzheimer's Disease (AD). The LASSI-L has distinguished those with amnestic mild cognitive impairment (aMCI) and high amyloid load from aMCI attributable to other non-AD conditions. The authors designed and implemented a web-based brief computerized version of the instrument, the LASSI-BC, to improve standardized administration, facilitate scoring accuracy, real-time data entry, and increase the accessibility of the measure.

The psychometric properties and clinical utility of the brief computerized version of the LASSI-L was evaluated, together with its ability to differentiate older adults who are cognitively normal (CN) from those with amnestic Mild Cognitive Impairment (aMCI).

After undergoing a comprehensive uniform clinical and neuropsychological evaluation using traditional measures, older adults were classified as cognitively normal or diagnosed with aMCI. All participants were administered the LASSI-BC, a computerized version of the LASSI-L. Test-retest and discriminant validity was assessed for each LASSI-BC subscale.

LASSI-BC subscales demonstrated high test-retest reliability, and discriminant validity was attained.

The LASSI-BC, a brief computerized version of the LASSI-L is a valid and useful cognitive tool for the detection of aMCI among older adults.
The LASSI-BC, a brief computerized version of the LASSI-L is a valid and useful cognitive tool for the detection of aMCI among older adults.
Due to an ageing demographic and rapid increase of cognitive impairment and dementia, combined with potential disease-modifying drugs and other interventions in the pipeline, there is a need for the development of accurate, accessible and efficient cognitive screening instruments, focused on early-stage detection of neurodegenerative disorders.

In this proof of concept report, we examine the validity of a newly developed digital cognitive test, the Geras Solutions Cognitive Test (GCST) and compare its accuracy against the Montreal Cognitive Assessment (MoCA).

106 patients, referred to the memory clinic, Karolinska University Hospital, due to memory complaints were included. All patients were assessed for presence of neurodegenerative disorder in accordance with standard investigative procedures. 66% were diagnosed with subjective cognitive impairment (SCI), 25% with mild cognitive impairment (MCI) and 9% fulfilled criteria for dementia. All patients were administered both MoCA and GSCT. Descriptive statistics and specificity, sensitivity and ROC curves were established for both test.

Mean score differed significantly between all diagnostic subgroups for both GSCT and MoCA (p<0.05). GSCT total test time differed significantly between all diagnostic subgroups (p<0.05). Overall, MoCA showed a sensitivity of 0.88 and specificity of 0.54 at a cut-off of <=26 while GSCT displayed 0.91 and 0.55 in sensitivity and specificity respectively at a cut-off of <=45.

This report suggests that GSCT is a viable cognitive screening instrument for both MCI and dementia.
This report suggests that GSCT is a viable cognitive screening instrument for both MCI and dementia.
Recent Alzheimer's disease (AD) trials have faced significant challenges to enroll pre-symptomatic or early stage AD subjects with biomarker positivity, minimal or no cognitive impairment, and likelihood to decline cognitively during a short trial period. Our previous study showed that digital cognitive biomarkers (DCB), generated by a hierarchical Bayesian cognitive process (HBCP) model, were able to distinguish groups of cognitively normal individuals with impending cognitive decline from those without. We generated DCBs using only baseline Auditory Verbal Learning Test's wordlist memory (WLM) item response data from the Mayo Clinic Alzheimer's Disease Patient Registry.

To replicate our previous findings, using baseline ADAS-Cog WLM item response data from the Alzheimer's Disease Neuroimaging Initiative, and compare DCBs to traditional approaches for scoring word-list memory tests.

Classified decliner subjects (n = 61) as those who developed amnestic MCI or AD dementia within 3 years of normal baseline, between impending cognitive decline and non-decline groups where individuals in both groups were classified as cognitively normal. This validated findings from our previous study, demonstrating DCBs' advantages over traditional approaches. This study warrants further refinement of the HBCP DCBs to predict impending cognitive decline in individuals and other factors associated with AD, such as physical biomarker load.
This study demonstrated DCBs' ability to distinguish , at baseline, between impending cognitive decline and non-decline groups where individuals in both groups were classified as cognitively normal. This validated findings from our previous study, demonstrating DCBs' advantages over traditional approaches. This study warrants further refinement of the HBCP DCBs to predict impending cognitive decline in individuals and other factors associated with AD, such as physical biomarker load.The Clinical Trials on Alzheimer's Disease (CTAD) 2020 conference was the stage for researchers from all over the world to present their recent and ongoing research focused on potential Alzheimer's disease (AD) treatments and prevention of cognitive decline. Among a varied range of topics, nutritional aspects arose as possibilities of treatments towards the promotion of a healthy aging. Among the discussed themes, supplementation of omega-3 polyunsaturated fatty acids and multi-nutrient approaches were presented, suggesting that long-term supplementation (i.e., over 3 years) might be needed for observing positive effects on cognitive performance. Trials testing ketogenic agents and carbohydrate-restricted diet were also presented and showed promising effects on improving cognitive function of mild-cognitive impaired (MCI) and pre-diabetic individuals, respectively, in a short-term way (i.e. after 3 to 6 months). The combination of some of the nutritional approaches with physical activity interventions raises the question on whether they would individually perform in a similar way. Promising therapies involving nutrition appear to be safe and well tolerated by volunteers. Failures on achieving positive findings raise questions on whether they were driven by specific characteristics of the studied populations, insufficient doses or duration of treatment. Notwithstanding, current evidence on the applicability of nutrition-based approaches as AD treatments are encouraging but demand further research on the topic.High-throughput experiments involving isolated droplets based on patterned superwettable surfaces are important for various applications related to biology, chemistry, and medicine, and they have attracted a large amount of interest. This paper provides a directional anchoring liquid-infused superamphiphobic surface (DAS), via combining concepts based on the droplet-anchoring behavior of beetle backs with patterned wettability, the directional adhesion of butterfly wings, and the slippery liquid-infused surfaces (SLISs) of pitcher plants. Regularly arranged ">"-shaped SLIS patterns were created on a superamphiphobic (SAM) background through ultrafast-laser-based technology. Improved directional anchoring abilities with a sliding angle difference of 77° were achieved; this is the largest sliding angle difference in a one-dimensional direction achieved using an artificial surface, to the best of the authors' knowledge. Thanks to the directional anchoring abilities, the DAS coupled droplet 'anchoring' and 'releasing' abilities. Furthermore, a high-throughput droplet manipulation device was designed, on which a micro-droplet array with a large number of droplets can be 'captured', 'transferred', or 'released' in a single step. With the addition of lubricant, the DAS can work continuously for even more than 30 cycles without cross-contamination between different droplets. The DAS also shows good stability under an ambient atmosphere and can maintain its functionality when manipulating corrosive droplets. The DAS and corresponding high-throughput droplet manipulation method are excellent candidates for practical applications.A simple method for determining total inorganic mercury (Hg) in solution using an enclosed quartz cell applying cold vapour-atomic absorption spectrometry (CV-AAS) can reduce sample volume, reductant, and cost compared with those of official methods. The Hg determination under the optimised conditions achieves precision similar to that of conventional CV-AAS.A facile procedure is reported for the synthesis of various 2-bromo-1-phenyl-5,6-dihydro-3H,7aH-benzo[b]pyrrolo[2,1-c][1,4]oxazin-3-ones via a radical bromination-induced ipso cyclization-ortho cyclization sequence of N-arylpropiolamides in the presence of TBAB and oxone. Milciclib The radical cyclization sequence involves a radical bromo α-addition into the alkyne, ipso-cyclization, and ortho-trapping of the spirocyclic intermediate.Current spherical surface registration methods achieve good performance on alignment and spatial normalization of cortical surfaces across individuals in neuroimaging analysis. However, they are computationally intensive, since they have to optimize an objective function independently for each pair of surfaces. In this paper, we present a fast learning-based algorithm that makes use of the recent development in spherical Convolutional Neural Networks (CNNs) for spherical cortical surface registration. Given a set of surface pairs without supervised information such as ground truth deformation fields or anatomical landmarks, we formulate the registration as a parametric function and learn its parameters by enforcing the feature similarity between one surface and the other one warped by the estimated deformation field using the function. Then, given a new pair of surfaces, we can quickly infer the spherical deformation field registering one surface to the other one. We model this parametric function using three orthogonal Spherical U-Nets and use spherical transform layers to warp the spherical surfaces, while imposing smoothness constraints on the deformation field. All the layers in the network are well-defined and differentiable, thus the parameters can be effectively learned. We show that our method achieves accurate cortical alignment results on 102 subjects, comparable to two state-of-the-art methods Spherical Demons and MSM, while runs much faster.
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