Characterization of pharmacists’ interventions in asthma management: A systematic review

© 2018 American Pharmacists Association® Objective: Pharmacists have adopted an active role in asthma management. This review aimed to analyze the intervention dose, understood as the “amount of program delivered,” and core components of the intervention provided by pharmacists in asthma management. Data sources: A literature search was conducted in December 2016 using PubMed. Study selection: A 2-stage approach was used. At the first stage, systematic reviews of pharmacists’ interventions in asthma management were identified. At the second stage, primary studies included in the systematic reviews were selected. Data extraction: The DEPICT-2 (Descriptive Elements of Pharmacist Intervention Characterization Tool) was used for data extraction. In addition GINA (Global Initiative for Asthma) guidelines were used as a reference to classify the interventions’ core components. Results: Thirty-one studies were included. In most of the studies, the pharmacist–patient intervention occurred at the community pharmacy setting (n = 22). The most common core components used in pharmacists’ interventions were the provision of drug information and patient counseling (n = 27). Pharmacists’ interventions frequently were targeted at assessing and improving the use of patient's inhaler technique (n = 27). Educational materials and written action plans were the materials most commonly used in the interventions (n = 20). The duration (n = 13) and the frequency (n = 16) of the intervention were the most frequent information about the intervention dose measure reported. Conclusion: Pharmacists’ interventions in asthma management are complex. Structured educational programs and patient counseling appear to be the most frequent core components of pharmacists’ interventions. Interventions were focused on providing information about the condition and on inhaler technique assessment and training. However, most studies failed to report the intervention dose sufficiently to be reproduced. The reporting of this indicator is crucial to ensure the reproducibility of the interventions assessed and their implementation in practice. (Registration number CRD42016029181.

Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation

Objective: Conduct a multicenter proof-of-concept clinical evaluation to assess the accuracy of an artificial intelligence system on a smartphone for automated detection of diabetic foot ulcers. Methods: The evaluation was undertaken with patients with diabetes (n = 81) from September 2020 to January 2021. A total of 203 foot photographs were collected using a smartphone, analysed using the artificial intelligence system, and compared against expert clinician judgement, with 162 images showing at least one ulcer, and 41 showing no ulcer. Sensitivity and specificity of the system against clinician decisions was determined and inter- and intra-rater reliability analysed. Results: Predictions/decisions made by the system showed excellent sensitivity (0.9157) and high specificity (0.8857). Merging of intersecting predictions improved specificity to 0.9243. High levels of inter- and intra-rater reliability for clinician agreement on the ability of the artificial intelligence system to detect diabetic foot ulcers was also demonstrated (Kα > 0.8000 for all studies, between and within raters). Conclusions: We demonstrate highly accurate automated diabetic foot ulcer detection using an artificial intelligence system with a low-end smartphone. This is the first key stage in the creation of a fully automated diabetic foot ulcer detection and monitoring system, with these findings underpinning medical device development

Dense active matter model of motion patterns in confluent cell monolayers

Epithelial cell monolayers show remarkable displacement and velocity correlations over distances of ten or more cell sizes that are reminiscent of supercooled liquids and active nematics. We show that many observed features can be described within the framework of dense active matter, and argue that persistent uncoordinated cell motility coupled to the collective elastic modes of the cell sheet is sufficient to produce swirl-like correlations. We obtain this result using both continuum active linear elasticity and a normal modes formalism, and validate analytical predictions with numerical simulations of two agent-based cell models, soft elastic particles and the self-propelled Voronoi model together with in-vitro experiments of confluent corneal epithelial cell sheets. Simulations and normal mode analysis perfectly match when tissue-level reorganisation occurs on times longer than the persistence time of cell motility. Our analytical model quantitatively matches measured velocity correlation functions over more than a decade with a single fitting parameter.Comment: updated version accepted for publication in Nat. Com

Assessment of the potential in vivo ecotoxicity of Double-Walled Carbon Nanotubes (DWNTs) in water, using the amphibian Ambystoma mexicanum

Because of their specific properties (mechanical, electrical, etc), carbon nanotubes (CNTs) are being assessed for inclusion in many manufactured products. Due to their massive production and number of potential applications, the impact of CNTs on the environment must be taken into consideration. The present investigation evaluates the ecotoxic potential of CNTs in the amphibian larvae (Ambystoma mexicanum). Acute toxicity and genotoxicity were analysed after 12 days of exposure in laboratory conditions. The genotoxic effects were analysed by scoring the micronucleated erythrocytes in the circulating blood of the larvae according to the French standard micronucleus assay. The results obtained in the present study demonstrated that CNTs are neither acutely toxic nor genotoxic to larvae whatever the CNTs concentration in the water, although black masses of CNTs were observed inside the gut. In the increasing economical context of CNTs, complementary studies must be undertaken, especially including mechanistic and environmental investigations

The innate immune sensor Toll-like receptor 2 controls the senescence-associated secretory phenotype

Cellular senescence is a stress response program characterized by a robust cell cycle arrest and the induction of a proinflammatory senescence-associated secretory phenotype (SASP) that is triggered through an unknown mechanism. Here, we show that, during oncogene-induced senescence (OIS), the Toll-like receptor 2 (TLR2) and its partner TLR10 are key mediators of senescence in vitro and in murine models. TLR2 promotes cell cycle arrest by regulating the tumor suppressors p53-p21 , p16 , and p15 and regulates the SASP through the induction of the acute-phase serum amyloids A1 and A2 (A-SAAs) that, in turn, function as the damage-associated molecular patterns (DAMPs) signaling through TLR2 in OIS. Last, we found evidence that the cGAS-STING cytosolic DNA sensing pathway primes TLR2 and A-SAAs expression in OIS. In summary, we report that innate immune sensing of senescence-associated DAMPs by TLR2 controls the SASP and reinforces the cell cycle arrest program in OIS

Hemozoin-mediated inflammasome activation limits long-lived anti-malarial immunity

During acute malaria, most individuals mount robust inflammatory responses that limit parasite burden. However, long-lived sterilizing anti-malarial memory responses are not efficiently induced, even following repeated Plasmodium exposures. Using multiple Plasmodium species, genetically modified parasites, and combinations of host genetic and pharmacologic approaches, we find that the deposition of the malarial pigment hemozoin directly limits the abundance and capacity of conventional type 1 dendritic cells to prime helper T cell responses. Hemozoin-induced dendritic cell dysfunction results in aberrant Plasmodium-specific CD4 T follicular helper cell differentiation, which constrains memory B cell and long-lived plasma cell formation. Mechanistically, we identify that dendritic cell-intrinsic NLRP3 inflammasome activation reduces conventional type 1 dendritic cell abundance, phagocytosis, and T cell priming functions in vivo. These data identify biological consequences of hemozoin deposition during malaria and highlight the capacity of the malarial pigment to program immune evasion during the earliest events following an initial Plasmodium exposure.Host-parasite interactio

Disturbed functional brain networks and neurocognitive function in low-grade glioma patients: a graph theoretical analysis of resting-state MEG

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study

Background: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. Methods: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. Results: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%–61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%–90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%–89%). Conclusion: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations

Modeling Sustainability Reporting with Ternary Attractor Neural Networks

International Conference on Mining Intelligence and Knowledge Exploration. Cluj-Napoca, Romania, December 20–22, 2018This work models the Corporate Sustainability General Reporting Initiative (GRI) using a ternary attractor network. A dataset of years evolution of the GRI reports for a world-wide set of companies was compiled from a recent work and adapted to match the pattern coding for a ternary attractor network. We compare the performance of the network with a classical binary attractor network. Two types of criteria were used for encoding the ternary network, i.e., a simple and weighted threshold, and the performance retrieval was better for the latter, highlighting the importance of the real patterns’ transformation to the three-state coding. The network exceeds the retrieval performance of the binary network for the chosen correlated patterns (GRI). Finally, the ternary network was proved to be robust to retrieve the GRI patterns with initial noise.This work has been supported by Spanish grants MINECO (http://www.mineco.gob.es/) TIN2014-54580-R, TIN2017-84452-R, and by UAMSantander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02