Niche differentiation in microbial communities with stable genomic traits over time in engineered systems.

Microbial communities in full-scale engineered systems undergo dynamic compositional changes. However, mechanisms governing assembly of such microbes and succession of their functioning and genomic traits under various environmental conditions are unclear. In this study, we used the activated sludge and anaerobic treatment systems of four full-scale industrial wastewater treatment plants as models to investigate the niches of microbes in communities and the temporal succession patterns of community compositions. High-quality representative metagenome-assembled genomes revealed that taxonomic, functional, and trait-based compositions were strongly shaped by environmental selection, with replacement processes primarily driving variations in taxonomic and functional compositions. Plant-specific indicators were associated with system environmental conditions and exhibited strong determinism and trajectory directionality over time. The partitioning of microbes in a co-abundance network according to groups of plant-specific indicators, together with significant between-group differences in genomic traits, indicated the occurrence of niche differentiation. The indicators of the treatment plant with rich nutrient input and high substrate removal efficiency exhibited a faster predicted growth rate, lower guanine-cytosine content, smaller genome size, and higher codon usage bias than the indicators of the other plants. In individual plants, taxonomic composition displayed a more rapid temporal succession than functional and trait-based compositions. The succession of taxonomic, functional, and trait-based compositions was correlated with the kinetics of treatment processes in the activated sludge systems. This study provides insights into ecological niches of microbes in engineered systems and succession patterns of their functions and traits, which will aid microbial community management to improve treatment performance.

Clinical predictors of rectal cancer response after neo-adjuvant (Chemo)Radiotherapy in Australia and New Zealand: Analysis of the Bi-National Colorectal Cancer Audit (BCCA).

Pathological complete response (pCR) is observed in 11-26% of locally advanced rectal cancers undergoing neoadjuvant chemoradiotherapy (nCRT). This study aims to determine pCR rates and clinicopathological predictors in the Australian and New Zealand (ANZ) cohort. The Bi-National Colorectal Cancer Audit (BCCA) was interrogated for all rectal cancer patients who underwent nCRT prior to surgical resection between 2007 and 2020. Patients were divided in two groups: pCR (AJCC tumour regression grade 0) and partial/no response (pPR, regression grade 1,2 or 3). In total, 3230 patients were included. Rates of pCR and pPR were 704 (21.8%) and 2526 (78.2%), respectively. Long-course nCRT (p < 0.0001), lower clinical tumour stage (cT; p < 0.0001), and nodal stage (cN; p = 0.003) were associated with pCR on univariate analysis. On multivariable analysis, cN0 stage and long-course nCRT remained independent factors for a pCR. Awareness of these predictors provides valuable information when counseling patients regarding prognosis and treatment options.

Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data.

Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM2.5 and PM10) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of âˆ¼ 60 %-80 % for bioaerosols and âˆ¼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data.

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

An intranasal ASO therapeutic targeting SARS-CoV-2.

The COVID-19 pandemic is exacting an increasing toll worldwide, with new SARS-CoV-2 variants emerging that exhibit higher infectivity rates and that may partially evade vaccine and antibody immunity. Rapid deployment of non-invasive therapeutic avenues capable of preventing infection by all SARS-CoV-2 variants could complement current vaccination efforts and help turn the tide on the COVID-19 pandemic. Here, we describe a novel therapeutic strategy targeting the SARS-CoV-2 RNA using locked nucleic acid antisense oligonucleotides (LNA ASOs). We identify an LNA ASO binding to the 5' leader sequence of SARS-CoV-2 that disrupts a highly conserved stem-loop structure with nanomolar efficacy in preventing viral replication in human cells. Daily intranasal administration of this LNA ASO in the COVID-19 mouse model potently suppresses viral replication (>80-fold) in the lungs of infected mice. We find that the LNA ASO is efficacious in countering all SARS-CoV-2 "variants of concern" tested both in vitro and in vivo. Hence, inhaled LNA ASOs targeting SARS-CoV-2 represents a promising therapeutic approach to reduce or prevent transmission and decrease severity of COVID-19 in infected individuals. LNA ASOs are chemically stable and can be flexibly modified to target different viral RNA sequences and could be stockpiled for future coronavirus pandemics.

High-Dose Thiamine Supplementation in Older Patients With Heart Failure: A Pilot Randomized Controlled Crossover Trial (THIAMINE-HF).

Background: Thiamine supplementation may improve cardiac function in older adults with heart failure (HF). Our objectives were to determine the following: (i) the feasibility of conducting a large trial of thiamine supplementation in HF; and (ii) the effects of thiamine on clinical outcomes. Methods: We conducted a double-blinded randomized placebo-controlled 2-period crossover feasibility study from June 2018 to April 2021. Adults aged ≥ 60 years with symptomatic HF and reduced ejection fraction (≤ 45%) were included. Participants were randomized to thiamine mononitrate 500 mg, or placebo, for 90 days and were switched to the opposite treatment for 90 days after a 6-week washout period. The primary feasibility outcome was recruitment of 24 participants in 11 months. Results: We screened 330 patients over 21 months to recruit 24 patients. Participants' mean age was 73.4 years. The targets for refusal rate, retention rate, and adherence rate were met. Nonsignificant improvements occurred in left ventricular ejection fraction and N-terminal pro-brain natriuretic peptide (NT-proBNP) level with thiamine. A total of 13 serious adverse events occurred in 7 patients; none were related to the study drug. Conclusions: Although we did not reach our recruitment target, we found high-dose thiamine supplementation to be well tolerated, with potential improvements in biomarker outcomes. A larger trial of thiamine supplementation is warranted.

Introduction: La supplémentation en thiamine peut améliorer la fonction cardiaque chez les personnes âgées atteintes d'insuffisance cardiaque (IC). Nos objectifs visaient à déterminer : (i) la faisabilité d'un essai de grande envergure sur la supplémentation en thiamine lors d'IC ; (ii) les effets de la thiamine sur les résultats cliniques. Méthodes: Nous avons réalisé une étude de faisabilité croisée à double insu et à répartition aléatoire contre placebo sur deux périodes de juin 2018 à avril 2021. Nous avons retenu les adultes de ≥ 60 ans qui avaient une IC symptomatique et une fraction d'éjection réduite (≤ 45 %). Nous avons réparti les participants de façon aléatoire pour recevoir 500 mg de mononitrate de thiamine ou le placebo durant 90 jours, et avons inversé le traitement durant 90 jours après une période de lavage de 6 semaines. Le principal critère de faisabilité était le recrutement de 24 participants en 11 mois. Résultats: Nous avons recruté 24 patients sur les 330 patients sélectionnés durant 21 mois. L'âge moyen des participants était de 73,4 ans. Les cibles des taux de refus, des taux de rétention et des taux d'adhésion ont été atteintes. Avec la thiamine, nous avons observé des améliorations non significatives de la fraction d'éjection ventriculaire gauche et de la concentration de propeptide natriurétique de type B N-terminal (NT-proBNP). Parmi les 13 événements indésirables sérieux qu'ont subis sept patients, aucun n'a été associé au médicament étudié. Conclusions: Bien que nous n'ayons pas atteint notre cible de recrutement, nous avons observé que la supplémentation en thiamine à dose élevée était bien tolérée et qu'il y avait des améliorations potentielles des résultats des biomarqueurs. Un essai de plus grande envergure sur la supplémentation en thiamine est justifié.

Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework.

Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework's success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.

City-Scale Meta-Analysis of Indoor Airborne Microbiota Reveals that Taxonomic and Functional Compositions Vary with Building Types.

Currently, there is a lack of understanding on the variations of the indoor airborne microbiotas of different building types within a city, and how operational taxonomic unit (OTU)- and amplicon sequence variant (ASV)-based analyses of the 16S rRNA gene sequences affect interpretation of the indoor airborne microbiota results. Therefore, in this study, the indoor airborne bacterial microbiotas between commercial buildings, residences, and subways within the same city were compared using both OTU- and ASV-based analytic methods. Our findings suggested that indoor airborne bacterial microbiota compositions were significantly different between building types regardless of the bioinformatics method used. The processes of ecological drift and random dispersal consistently played significant roles in the assembly of the indoor microbiota across building types. Abundant taxa tended to be more centralized in the correlation network of each building type, highlighting their importance. Taxonomic changes between the microbiotas of different building types were also linked to changes in their inferred metabolic function capabilities. Overall, the results imply that customized strategies are necessary to manage indoor airborne bacterial microbiotas for each building type or even within each specific building.

Metagenomic insights into the microbial communities of inert and oligotrophic outdoor pier surfaces of a coastal city.

BACKGROUND: Studies of the microbiomes on surfaces in built environment have largely focused on indoor spaces, while outdoor spaces have received far less attention. Piers are engineered infrastructures commonly found in coastal areas, and due to their unique locations at the interface between terrestrial and aquatic ecosystems, pier surfaces are likely to harbor interesting microbiology. In this study, the microbiomes on the metal and concrete surfaces at nine piers located along the coastline of Hong Kong were investigated by metagenomic sequencing. The roles played by different physical attributes and environmental factors in shaping the taxonomic composition and functional traits of the pier surface microbiomes were determined. Metagenome-assembled genomes were reconstructed and their putative biosynthetic gene clusters were characterized in detail. RESULTS: Surface material was found to be the strongest factor in structuring the taxonomic and functional compositions of the pier surface microbiomes. Corrosion-related bacteria were significantly enriched on metal surfaces, consistent with the pitting corrosion observed. The differential enrichment of taxa mediating biodegradation suggests differences between the metal and concrete surfaces in terms of specific xenobiotics being potentially degraded. Genome-centric analysis detected the presence of many novel species, with the majority of them belonging to the phylum Proteobacteria. Genomic characterization showed that the potential metabolic functions and secondary biosynthetic capacity were largely correlated with taxonomy, rather than surface attributes and geography. CONCLUSIONS: Pier surfaces are a rich reservoir of abundant novel bacterial species. Members of the surface microbial communities use different mechanisms to counter the stresses under oligotrophic conditions. A better understanding of the outdoor surface microbiomes located in different environments should enhance the ability to maintain outdoor surfaces of infrastructures. Video Abstract.

Burnout among Hospital Pharmacists: Prevalence, Self-Awareness, and Preventive Programs in Pharmacy School Curricula.

BACKGROUND: Clinician burnout is a work-related syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment. It is associated with reduced quality of care, as well as the occurrence of medical errors and mental illness. Although burnout has been extensively studied in populations of physicians and nurses, there is limited research assessing burnout in pharmacists and their exposure to burnout-related education. OBJECTIVES: To determine the prevalence of burnout and its associated risk factors among hospital pharmacists and to explore the status of preventive programs in pharmacy school curricula. METHODS: A cross-sectional online survey was conducted with hospital pharmacists working in the province of Ontario, Canada. Respondents completed the Maslach Burnout Inventory (MBI) and responded to questions about career characteristics and professional satisfaction. A multivariable regression analysis was used to determine factors independently associated with burnout. In addition, all pharmacy schools in Canada were surveyed electronically about their burnout-prevention curricula. RESULTS: Of 2465 hospital pharmacists in Ontario, 270 responded (11% response rate). Most respondents were women (77% [195/252]) and were working full-time (90% [227/252]), with a substantial proportion working in the acute care setting (39% [96/246]). The burnout rate was 61.1% (165/270; 95% confidence interval 55.5%-66.8%). Factors independently associated with burnout were dissatisfaction with work-life balance (odds ratio [OR] 2.62, p = 0.005) and feeling that contributions were unappreciated (OR 2.60, p = 0.019). Of those whose MBI score indicated burnout, 23% (36/158) were not aware of experiencing burnout. All 10 Canadian pharmacy schools responded to the survey, with 9 (90%) reporting that they did not have burnout-prevention curricula; however, 8 (80%) reported interest in incorporating such material. CONCLUSIONS: The rate of burnout among hospital pharmacists in Ontario was high, and preventive action is needed. Opportunities exist to both improve pharmacists' resilience at the undergraduate level and reduce institutional stressors in the workplace.

CONTEXTE: L'épuisement professionnel du clinicien est un syndrome lié au travail qui se caractérise par une fatigue émotionnelle, une dépersonnalisation et l'amoindrissement des réalisations personnelles. Il est associé à la réduction de la qualité des soins, à la survenance d'erreurs médicales et à la maladie mentale. Bien que ce sujet ait fait l'objet d'études approfondies dans les populations de médecins et d'infirmiers, les recherches qui se penchent sur l'épuisement des pharmaciens et la possibilité qui leur est offerte de bénéficier de formations relatives à l'épuisement sont limitées. OBJECTIFS: Déterminer la prévalence du surmenage professionnel et des facteurs de risque qui lui sont associés parmi les pharmaciens d'hôpitaux et examiner les programmes de prévention dans les formations en école de pharmacie. MÉTHODE: Une enquête transversale en ligne a été menée auprès des pharmaciens hospitaliers travaillant en Ontario, au Canada. Les répondants ont rempli le Maslach Burnout Inventory (MBI) [Evaluation du syndrome de l'épuisement professionnel de Maslach] et répondu à des questions portant sur les caractéristiques d'emploi et la satisfaction professionnelle. Une analyse de régression multivariable a permis de déterminer les facteurs indépendamment associés à l'épuisement. De plus, une enquête électronique portant sur le programme de prévention de l'épuisement a été menée dans toutes les écoles de pharmacie au Canada. RÉSULTATS: Sur les 2465 pharmaciens d'hôpitaux en Ontario, 270 ont répondu (taux de réponse de 11 %). La plupart des répondants étaient des femmes (77 % [195/252]) travaillant à temps plein (90 % [227/252]); une part importante travaillait dans un environnement de soins aigus (39 % [96/246]). Le taux d'épuisement était de 61,1 % (165/270, intervalle de confiance 95 % 55,5 %­66,8 %). Les facteurs indépendamment associés à l'épuisement étaient l'insatisfaction liée à l'équilibre entre sa vie professionnelle et sa vie personnelle (rapport de cotes [RC] 2,62, p = 0,005) et l'impression d'un manque d'appréciation de sa contribution (RC 2,60, p = 0,019). Parmi les personnes dont le score MBI indiquait un épuisement professionnel, 23 % (36/158) ne savaient pas qu'elles en étaient victimes. Les dix écoles de pharmacie canadiennes ont répondu à l'enquête et neuf (90 %) ont rapporté ne pas avoir de programme axé sur la prévention de l'épuisement professionnel, cependant, huit (80 %) ont montré leur intérêt pour un tel programme. CONCLUSIONS: Le taux d'épuisement professionnel parmi les pharmaciens d'hôpitaux en Ontario était élevé et des actions préventives sont nécessaires. Les possibilités existent pour améliorer la résilience des pharmaciens au niveau du premier cycle universitaire et réduire les facteurs de stress institutionnels sur le lieu de travail.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. RESULTS: We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. CONCLUSIONS: SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism.

BACKGROUND: The systems-scale analysis of cellular metabolites, "metabolomics," provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA), which makes assumptions that preclude direct integration of metabolomics data into the underlying models. Finding a way to retain the advantages of FBA's linear structure while relaxing some of its assumptions could allow us to account for metabolite levels and metabolite-dependent regulation in strain design tools built from FBA, improving the accuracy of predictions made by these tools. We designed, implemented, and characterized a modeling strategy based on Dynamic FBA (DFBA), called Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), to satisfy these specifications. Our strategy adds constraints describing the dynamics and regulation of metabolism that are strictly linear. We evaluated LK-DFBA against alternative modeling frameworks using simulated noisy data from a small in silico model and a larger model of central carbon metabolism in E. coli, and compared each framework's ability to recapitulate the original system. RESULTS: In the smaller model, we found that we could use regression from a dynamic flux estimation (DFE) with an optional non-linear parameter optimization to reproduce metabolite concentration dynamic trends more effectively than an ordinary differential equation model with generalized mass action rate laws when tested under realistic data sampling frequency and noise levels. We observed detrimental effects across all tested modeling approaches when metabolite time course data were missing, but found these effects to be smaller for LK-DFBA in most cases. With the E. coli model, we produced qualitatively reasonable results with similar properties to the smaller model and explored two different parameterization structures that yield trade-offs in computation time and accuracy. CONCLUSIONS: LK-DFBA allows for calculation of metabolite concentrations and considers metabolite-dependent regulation while still retaining many computational advantages of FBA. This provides the proof-of-principle for a new metabolic modeling framework with the potential to create genome-scale dynamic models and the potential to be applied in strain engineering tools that currently use FBA.

An ANGPTL4-ceramide-protein kinase Cζ axis mediates chronic glucocorticoid exposure-induced hepatic steatosis and hypertriglyceridemia in mice.

Chronic or excess glucocorticoid exposure causes lipid disorders such as hypertriglyceridemia and hepatic steatosis. Angptl4 (angiopoietin-like 4), a primary target gene of the glucocorticoid receptor in hepatocytes and adipocytes, is required for hypertriglyceridemia and hepatic steatosis induced by the synthetic glucocorticoid dexamethasone. Angptl4 has also been shown to be required for dexamethasone-induced hepatic ceramide production. Here, we further examined the role of ceramide-mediated signaling in hepatic dyslipidemia caused by chronic glucocorticoid exposure. Using a stable isotope-labeling technique, we found that dexamethasone treatment induced the rate of hepatic de novo lipogenesis and triglyceride synthesis. These dexamethasone responses were compromised in Angptl4-null mice (Angptl4-/-). Treating mice with myriocin, an inhibitor of the rate-controlling enzyme of de novo ceramide synthesis, serine palmitoyltransferase long-chain base subunit 1 (SPTLC1)/SPTLC2, decreased dexamethasone-induced plasma and liver triglyceride levels in WT but not Angptl4-/- mice. We noted similar results in mice infected with adeno-associated virus-expressing small hairpin RNAs targeting Sptlc2. Protein phosphatase 2 phosphatase activator (PP2A) and protein kinase Cζ (PKCζ) are two known downstream effectors of ceramides. We found here that mice treated with an inhibitor of PKCζ, 2-acetyl-1,3-cyclopentanedione (ACPD), had lower levels of dexamethasone-induced triglyceride accumulation in plasma and liver. However, small hairpin RNA-mediated targeting of the catalytic PP2A subunit (Ppp2ca) had no effect on dexamethasone responses on plasma and liver triglyceride levels. Overall, our results indicate that chronic dexamethasone treatment induces an ANGPTL4-ceramide-PKCζ axis that activates hepatic de novo lipogenesis and triglyceride synthesis, resulting in lipid disorders.

Deprescribing benzodiazepine receptor agonists taken for insomnia: a review and key messages from practice guidelines.

Long­term benzodiazepine receptor agonist (BZRA) use for insomnia is common and highly prevalent in adults in all care settings. Evidence syntheses suggest that the therapeutic benefits of benzodiazepines for insomnia are marginal and very short term. On the harm side, BZRAs are associated with daytime sedation and confusion. Long­term use increases the risk of falls, fractures, cognitive impairment, and motor vehicle accidents. An evidence­based clinical practice guideline has been developed to assist with deprescribing BZRAs. This review highlights the rationale for deprescribing BZRAs used for insomnia and summarizes key messages for clinicians from the new BZRA deprescribing guideline and their supporting evidence.

Managing High-Cost Healthcare Users: The International Search for Effective Evidence-Supported Strategies.

High-cost healthcare users (HCUs) are a small proportion of the population who use a disproportionate amount of healthcare resources. Although the phenomenon occurs across the entire age spectrum, older adults represent the majority of HCUs. HCUs have drawn increasing attention internationally from clinicians, health policy-makers, and government administrators. Many experts have suggested that the short- and long-term sustainability of the healthcare system is threatened unless current approaches to the care and healthcare costs of this population are modified. Complex case management and care coordination models are being implemented internationally to address HCUs despite a lack of strong evidence to support their effectiveness in improving clinical outcomes or savings in costs of care. We review what is known about HCUs and the available evidence for the effectiveness of interventions designed to manage their high and costly healthcare use.

NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data.

INTRODUCTION: A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation. OBJECTIVES: Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations. METHODS: We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data. RESULTS: Our results show that NS-kNN typically outperforms kNN when at least 20-30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR. CONCLUSION: Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.

A peptide functionalized poly(ethylene glycol) (PEG) hydrogel for investigating the influence of biochemical and biophysical matrix properties on tumor cell migration.

To address the challenges associated with defined control over matrix properties in 3D cell culture systems, we employed a peptide functionalized poly(ethylene glycol) (PEG) hydrogel matrix in which mechanical modulus and adhesive properties were tuned. An HT-1080 human fibrosarcoma cell line was chosen as a model for probing matrix influences on tumor cell migration using the PEG hydrogel platform. HT-1080 speed varied with a complex dependence on both matrix modulus and Cys-Arg-Gly-Asp-Ser (CRGDS) adhesion ligand concentration, with regimes in which motility increased, decreased, or was minimally altered being observed. We further investigated cell motility by forming matrix interfaces that mimic aspects of tissue boundaries that might be encountered during invasion by taking advantage of the spatial control of the thiol-ene photochemistry to form patterned regions of low and high cross-linking densities. HT-1080s in 100 Pa regions of patterned PEG hydrogels tended to reverse direction or aggregate at the interface when they encountered a 360 Pa boundary. In contrast, HT-1080s were apparently unimpeded when migrating from the stiff to the soft regions of PEG peptide hydrogels, which may indicate that cells are capable of "reverse durotaxis" within at least some matrix regimes. Taken together, our results identified matrix regimes in which HT-1080 motility was both positively and negatively influenced by cell adhesion or matrix modulus.

Engineering Escherichia coli for light-activated cytolysis of mammalian cells.

By delivering payloads in response to specific exogenous stimuli, smart bacterial therapeutics have the potential to overcome many limitations of conventional therapies, including poor targeting specificity and dosage control in current cancer treatments. Although not yet explored as a trigger for bacterial drug delivery, light is an ideal induction mechanism because it offers fine spatiotemporal control and is easily and safely administered. Using recent advances in optogenetics, we have engineered two strains of Escherichia coli to secrete a potent mammalian cytotoxin in response to blue or red light. The tools in this study demonstrate the initial feasibility of light-activated bacterial therapeutics for applications such as tumor cytolysis, and their modular nature should enable simple substitution of other payloads of interest.

A quantitative comparison of human HT-1080 fibrosarcoma cells and primary human dermal fibroblasts identifies a 3D migration mechanism with properties unique to the transformed phenotype.

Here, we describe an engineering approach to quantitatively compare migration, morphologies, and adhesion for tumorigenic human fibrosarcoma cells (HT-1080s) and primary human dermal fibroblasts (hDFs) with the aim of identifying distinguishing properties of the transformed phenotype. Relative adhesiveness was quantified using self-assembled monolayer (SAM) arrays and proteolytic 3-dimensional (3D) migration was investigated using matrix metalloproteinase (MMP)-degradable poly(ethylene glycol) (PEG) hydrogels ("synthetic extracellular matrix" or "synthetic ECM"). In synthetic ECM, hDFs were characterized by vinculin-containing features on the tips of protrusions, multipolar morphologies, and organized actomyosin filaments. In contrast, HT-1080s were characterized by diffuse vinculin expression, pronounced ß1-integrin on the tips of protrusions, a cortically-organized F-actin cytoskeleton, and quantitatively more rounded morphologies, decreased adhesiveness, and increased directional motility compared to hDFs. Further, HT-1080s were characterized by contractility-dependent motility, pronounced blebbing, and cortical contraction waves or constriction rings, while quantified 3D motility was similar in matrices with a wide range of biochemical and biophysical properties (including collagen) despite substantial morphological changes. While HT-1080s were distinct from hDFs for each of the 2D and 3D properties investigated, several features were similar to WM239a melanoma cells, including rounded, proteolytic migration modes, cortical F-actin organization, and prominent uropod-like structures enriched with ß1-integrin, F-actin, and melanoma cell adhesion molecule (MCAM/CD146/MUC18). Importantly, many of the features observed for HT-1080s were analogous to cellular changes induced by transformation, including cell rounding, a disorganized F-actin cytoskeleton, altered organization of focal adhesion proteins, and a weakly adherent phenotype. Based on our results, we propose that HT-1080s migrate in synthetic ECM with functional properties that are a direct consequence of their transformed phenotype.