Size exclusion and affinity-based removal of nanoparticles with electrospun cellulose acetate membranes infused with functionalized cellulose nanocrystals

Abstract Membrane filtration and affinity-based adsorption are the two most used strategies in separation technologies. Here, µm-thick multifunctional and sustainable composite membranes of electrospun cellulose acetate (CA) infused with functionalized, anionic, and cationic cellulose nanocrystals (CNCs) with enhanced wettability, tensile strength, and excellent retention capacities were designed. CNCs could uniformly impregnate into the three-dimensional CA network to effectively improve its properties. The impregnation of cationic CNCs at 0.5 wt% concentration drastically increased the tensile strength (1669%) while maintaining high permeation flux of 9400 Lm-2h-1 which is remarkable with cellulose modified electrospun membranes. The membranes infused with anionic CNCs exhibited a particle retention efficiency of 96% for 500 nm and 77% for 100 nm latex beads whilst the cationic CNC membranes exhibited a combined particle retention strategy using selectivity and size exclusion with a retention of >81% with 100 nm latex beads and 80% with ∼50 nm silver nanoparticles. We envision that the developed multifunctional membranes can be utilized for affinity-based and size-exclusion filtration to selectively trap bacteria or substances of biological significance

Findings and Future Directions from a Smoking Cessation Trial Utilizing a Clinical Decision Support Tool

Background Tobacco smoking is the leading cause of disease, death, and disability in the United States. Dental practitioners are advised to provide evidence-based smoking cessation interventions to their patients, yet dental practitioners frequently fail to deliver brief smoking cessation advice. Objectives To test whether giving dental practitioners a clinical decisions support (CDS) system embedded in their electronic dental record would increase the rate at which patients who smoke 1) report receiving a brief intervention or referral to treatment during a recent dental visit, 2) taking action related to smoking cessation within 7 days of visit, and 3) stop smoking for one day or more or reduce the amount smoked by 50% within 6 months. Methods Two-group, parallel arm, cluster-randomized trial. From March through December 2019, 15 non-academic primary care dental clinics were randomized via covariate adaptive randomization to either a usual care arm or the CDS arm. Adult smokers completed an initial telephone survey within 7 days of their visit and another survey after 6 months. Results Forty-three patients from 5 CDS and 13 patients from 2 usual care clinics completed the 7-day survey. While the proportion of patients who reported receipt of a brief intervention or referral to treatment was significantly greater in the CDS arm than the usual care arm (84.3% versus 58.6%; p = 0.005), the differences in percentage of patients who took any action related to smoking cessation within 7 days (44.4% versus 22.3%; p= 0.077), or stopped smoking for one day or more and/or reduced amount smoked by 50% within 6 months (63.1% versus 46.2%; p = 0.405) were large but not statistically significant. Conclusions Despite interruption by Covid-19, these results demonstrate a promising approach to assist dental practitioners in providing their patients with smoking cessation screening, brief intervention and referral to treatment

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/sci