Sammer Marzouk

CALF-20, a Zn-triazolate-based metal–organic framework (MOF), is one of the most promising adsorbent materials for CO2 capture. However, competitive adsorption of water severely limits its performance when the relative humidity (RH) exceeds 40%, limiting the potential implementation of CALF-20 in practical settings where CO2 is saturated with moisture, such as postcombustion flue gas. In this work, three newly designed MOFs related to CALF-20, denoted as NU-220, CALF-20M-w, and CALF-20M-e that feature hydrophobic methyltriazolate linkers, are presented. Inclusion of methyl groups in the linker is proposed as a strategy to improve the uptake of CO2 in the presence of water. Notably, both CALF-20M-w and CALF-20M-e retain over 20% of their initial CO2 capture efficiency at 70% RH─a threshold at which CALF-20 shows negligible CO2 uptake. Grand canonical Monte Carlo simulations reveal that the methyl group hinders water network formation in the pores of CALF-20M-w and CALF-20M-e and enhances their CO2 selectivity over N2 in the presence of a high moisture content. Moreover, calculated radial distribution functions indicate that introducing the methyl group into the triazolate linker increases the distance between water molecules and Zn coordination bonds, offering insights into the origin of the enhanced moisture stability observed for CALF-20M-w and CALF-20M-e relative to CALF-20. Overall, this straightforward design strategy has afforded more robust sorbents that can potentially meet the challenge of effectively capturing CO2 in practical industrial applications.

Objective: To develop standardization for nomenclature, diagnostic work up and diagnostic criteria for cases of neurocognitive regression in Down syndrome. Background: There are no consensus criteria for the evaluation or diagnosis of neurocognitive regression in persons with Down syndrome. As such, previously published data on this condition is relegated to smaller case series with heterogenous data sets. Lack of standardized assessment tools has slowed research in this clinical area. Methods: The authors performed a two-round traditional Delphi method survey of an international group of clinicians with experience in treating Down syndrome to develop a standardized approach to clinical care and research in this area. Thirty-eight potential panelists who had either previously published on neurocognitive regression in Down syndrome or were involved in national or international working groups on this condition were invited to participate. In total, 27 panelists (71%) represented nine medical specialties and six different countries reached agreement on preliminary standards in this disease area. Moderators developed a proposed nomenclature, diagnostic work up and diagnostic criteria based on previously published reports of regression in persons with Down syndrome. Results: During the first round of survey, agreement on nomenclature for the condition was reached with 78% of panelists agreeing to use the term Down Syndrome Regression Disorder (DSRD). Agreement on diagnostic work up and diagnostic criteria was not reach on the first round due to low agreement amongst panelists with regards to the need for neurodiagnostic testing. Following incorporation of panelist feedback, diagnostic criteria were agreed upon (96% agreement on neuroimaging, 100% agreement on bloodwork, 88% agreement on lumbar puncture, 100% agreement on urine studies, and 96% agreement on "other" studies) as were diagnostic criteria (96% agreement). Conclusions: The authors present international consensus agreement on the nomenclature, diagnostic work up, and diagnostic criteria for DSRD, providing an initial practical framework that can advance both research and clinical practices for this condition.

Conventional means of Parkinson's Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

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