Influenza A virus NS1 effector domain is required for PA-X-mediated host shutoff in infected cells.

Many viruses inhibit general host gene expression to limit innate immune responses and gain preferential access to the cellular translational apparatus for their protein synthesis. This process is known as host shutoff. Influenza A viruses (IAVs) encode two host shutoff proteins: nonstructural protein 1 (NS1) and polymerase acidic X (PA-X). NS1 inhibits host nuclear pre-messenger RNA maturation and export, and PA-X is an endoribonuclease that preferentially cleaves host spliced nuclear and cytoplasmic messenger RNAs. Emerging evidence suggests that in circulating human IAVs NS1 and PA-X co-evolve to ensure optimal magnitude of general host shutoff without compromising viral replication that relies on host cell metabolism. However, the functional interplay between PA-X and NS1 remains unexplored. In this study, we sought to determine whether NS1 function has a direct effect on PA-X activity by analyzing host shutoff in A549 cells infected with wild-type or mutant IAVs with NS1 effector domain deletion. This was done using conventional quantitative reverse transcription polymerase chain reaction techniques and direct RNA sequencing using nanopore technology. Our previous research on the molecular mechanisms of PA-X function identified two prominent features of IAV-infected cells: nuclear accumulation of cytoplasmic poly(A) binding protein (PABPC1) and increase in nuclear poly(A) RNA abundance relative to the cytoplasm. Here we demonstrate that NS1 effector domain function augments PA-X host shutoff and is necessary for nuclear PABPC1 accumulation. By contrast, nuclear poly(A) RNA accumulation is not dependent on either NS1 or PA-X-mediated host shutoff and is accompanied by nuclear retention of viral transcripts. Our study demonstrates for the first time that NS1 and PA-X may functionally interact in mediating host shutoff.IMPORTANCERespiratory viruses including the influenza A virus continue to cause annual epidemics with high morbidity and mortality due to the limited effectiveness of vaccines and antiviral drugs. Among the strategies evolved by viruses to evade immune responses is host shutoff-a general blockade of host messenger RNA and protein synthesis. Disabling influenza A virus host shutoff is being explored in live attenuated vaccine development as an attractive strategy for increasing their effectiveness by boosting antiviral responses. Influenza A virus encodes two proteins that function in host shutoff: the nonstructural protein 1 (NS1) and the polymerase acidic X (PA-X). We and others have characterized some of the NS1 and PA-X mechanisms of action and the additive effects that these viral proteins may have in ensuring the blockade of host gene expression. In this work, we examined whether NS1 and PA-X functionally interact and discovered that NS1 is required for PA-X to function effectively. This work significantly advances our understanding of influenza A virus host shutoff and identifies new potential targets for therapeutic interventions against influenza and further informs the development of improved live attenuated vaccines.

A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models.

OBJECTIVE: Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model's potential to introduce bias. MATERIALS AND METHODS: Our methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist. RESULTS: We selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical ways in which these algorithms can perpetuate health care inequalities. We found that LACE and HOSPITAL have the greatest potential for introducing bias, Johns Hopkins ACG has the most areas of uncertainty, and HATRIX has the fewest causes for concern. DISCUSSION: Our approach gives model developers and health care providers a practical and systematic method for evaluating bias in predictive models. Traditional bias identification methods do not elucidate sources of bias and are thus insufficient for mitigation efforts. With our checklist, bias can be addressed and eliminated before a model is fully developed or deployed. CONCLUSION: The potential for algorithms to perpetuate biased outcomes is not isolated to readmission prediction models; rather, we believe our results have implications for predictive models across health care. We offer a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams.

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework's ability to closely simulate the readmission risk trajectories for cancer patients.

Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption.

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

Reliability and Feasibility of the First-Episode Psychosis Services Fidelity Scale-Revised for Remote Assessment.

OBJECTIVE: The authors sought to evaluate the interrater reliability and feasibility of the First-Episode Psychosis Services Fidelity Scale-Revised (FEPS-FS-R) for remote assessment of first-episode psychosis programs according to the coordinated specialty care model. METHODS: The authors used the FEPS-FS-R to assess the fidelity of 36 first-episode psychosis program sites in the United States with information from three sources: administrative data, health record review, and phone interviews with staff. Four raters independently conducted fidelity assessments of five program sites by listening to each of the staff interviews and independently rating the two other data sources from each site. To calculate interrater reliability, the authors used intraclass correlation coefficients (ICCs) for each of the five sites and across the total scores for each site. RESULTS: Total interrater reliability was in the good to excellent range, with a mean ICC of 0.91 (95% confidence interval = 0.72-0.99, p<0.001). Two first-episode psychosis program sites (6%) achieved excellent fidelity, 25 (69%) good fidelity, and nine (25%) fair fidelity. Of the 32 distinct items on the FEPS-FS-R, 23 (72%) were used with good or excellent fidelity. Most sites achieved high fidelity on most items, but five items received ratings indicating low-fidelity use at most sites. The fidelity assessment proved feasible, and sites required on average 10.5 hours for preparing and conducting the fidelity review. CONCLUSIONS: The FEPS-FS-R has high interrater reliability and can differentiate high-, moderate-, and low-fidelity sites. Most sites had good overall fidelity, but the FEPS-FS-R identified some services that were challenging to implement at many sites.

Psychometric Properties of the General Organizational Index (GOI): A Measure of Individualization and Quality Improvement to Complement Program Fidelity.

To assess the implementation of effective practices, mental health programs need standardized measures. The General Organizational Index (GOI), although widely used for this purpose, has received minimal psychometric research. For this study, we assessed psychometric properties of the GOI scale administered four times over 18 months during the implementation of a new program in 11 sites. The GOI scale demonstrated high levels of interrater reliability (.97), agreement between assessors on item ratings (86% overall), internal consistency (.77-.80 at three time points), sensitivity to change, and feasibility. We conclude that the GOI scale has acceptable psychometric properties, and its use may enhance implementation and research on evidence-based mental health practices.Trial registration: REK2015/2169. ClinicalTrials.gov Identifier: NCT03271242.

The Physical Health Care Fidelity Scale: Psychometric Properties.

Mental health programs need an instrument to monitor adherence to evidence-based physical health care for people with serious mental illness. The paper describes the Physical Health Care Fidelity Scale and study interrater reliability, frequency distribution, sensitivity to change and feasibility. Four fidelity assessments were conducted over 18 months at 13 sites randomized to implementation support for evidence-based physical health care. We found good to excellent interrater reliability, adequate sensitivity for change, good feasibility and wide variability in fidelity across sites after 18 months of implementation. Programs were more successful in establishing Policies stating physical health care standards than in implementing these Policies. The Physical Health Care Fidelity Scale measures and guides implementation of evidence-based physical health care reliably.Trial registration: ClinicalTrials.gov Identifier: NCT03271242.

The Antipsychotic Medication Management Fidelity Scale: Psychometric properties.

The paper describes the Antipsychotic Medication Management Fidelity Scale and its psychometric properties, including interrater reliability, frequency distribution, sensitivity to change and feasibility. Fidelity assessors conducted fidelity reviews four times over 18 months at eight sites receiving implementation support for evidence-based antipsychotic medication management. Data analyses shows good to fair interrater reliability, adequate sensitivity to change over time and good feasibility. At 18 months, item ratings varied from poor to full fidelity on most items. Use of the scale can assess fidelity to evidence-based guidelines for antipsychotic medication management and guide efforts to improve practice. Further research should improve and better calibrate some items, and improve the procedures for access to information.Trial registration: ClinicalTrials.gov Identifier: NCT03271242.

Psychometric Properties of a Fidelity Scale for Illness Management and Recovery.

This study examined the psychometric properties and feasibility of the Illness Management and Recovery (IMR) Fidelity scale. Despite widespread use of the scale, the psychometric properties have received limited attention. Trained fidelity assessors conducted assessments four times over 18 months at 11 sites implementing IMR. The IMR Fidelity scale showed excellent interrater reliability (.99), interrater item agreement (94%), internal consistency (.91-.95 at three time points), and sensitivity to change. Frequency distributions generally showed that item ratings included the entire range. The IMR Fidelity scale has excellent psychometric properties and should be used to evaluate and guide the implementation of IMR.Trial registration: ClinicalTrials.gov Identifier: NCT03271242.

Combined application of QEM-SEM and hard X-ray microscopy to determine mineralogical associations and chemical speciation of trace metals.

We describe the application of quantitative evaluation of mineralogy by scanning electron microscopy in combination with techniques commonly available at hard X-ray microprobes to define the mineralogical environment of a bauxite residue core segment with the more specific aim of determining the speciation of trace metals (e.g., Ti, V, Cr, and Mn) within the mineral matrix. Successful trace metal speciation in heterogeneous matrices, such as those encountered in soils or mineral residues, relies on a combination of techniques including spectroscopy, microscopy, diffraction, and wet chemical and physical experiments. Of substantial interest is the ability to define the mineralogy of a sample to infer redox behavior, pH buffering, and mineral-water interfaces that are likely to interact with trace metals through adsorption, coprecipitation, dissolution, or electron transfer reactions. Quantitative evaluation of mineralogy by scanning electron microscopy coupled with micro-focused X-ray diffraction, micro-X-ray fluorescence, and micro-X-ray absorption near edge structure (mXANES) spectroscopy provided detailed insights into the composition of mineral assemblages and their effect on trace metal speciation during this investigation. In the sample investigated, titanium occurs as poorly ordered ilmenite, as rutile, and is substituted in iron oxides. Manganese's spatial correlation to Ti is closely linked to ilmenite, where it appears to substitute for Fe and Ti in the ilmenite structure based on its mXANES signature. Vanadium is associated with ilmenite and goethite but always assumes the +4 oxidation state, whereas chromium is predominantly in the +3 oxidation state and solely associated with iron oxides (goethite and hematite) and appears to substitute for Fe in the goethite structure.