194 research outputs found
The limits of relational governance: Sales force strategies in the U.S. medical device industry
Research Summary: We explore how inter-organizational relationships shape firm boundary decisions. Using data on 545 U.S. medical device manufacturers’ product portfolios and sales governance choices (i.e., internal or external sales forces) from 1983 to 1996, we find relational capital between manufacturers and external sales forces influences future firm boundary decisions. Relational capital lowers the likelihood of integrating the sales function, but only when firms remain focused on the same product market. Further, launching an innovative product has a nuanced effect. For firms lacking relational capital, innovation increases the likelihood of sales integration. This pattern reverses as relational capital accumulates, but only when innovations are in the firm’s existing focal product market. Our findings suggest important
limits on the effect of relational governance on firm strategy.
Managerial Abstract: Choosing between in-house or external sales is a key strategic decision. In the medical device industry, this decision is particularly important because sales people are conduits between R&D and customers. For firms who initially choose external sales, the tradeoff between maintaining existing links (via external sales) and developing new, direct relationships (by bringing sales in-house) can change significantly as product portfolios change. Analyzing 545 U.S. medical device manufacturers from 1983 to 1996, we find that existing relationships with external sales forces reduce the likelihood of bringing sales in-house, but only
when firms remain in the same product market, such as orthopedic implants. When firms launch products in new markets, especially innovations, they are more likely to bring sales in-house
The self-report version and digital format of the COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) for Long Covid or Post-COVID syndrome assessment and monitoring
The C19-YRS was the first scale reported in the literature for patient assessment and monitoring in Long Covid or Post-COVID syndrome. The scale has demonstrated content validity in a previous COVID-19 follow-up study. The growing number of patients with Post-COVID syndrome required the development of a self-report version (and a digital format) so that the scale can be completed by patients themselves. Individuals with Long Covid and clinicians providing care were involved in iterative changes to the scale. The self-report version of the scale captures symptom severity, functional disability and global health status. The C19-YRS digital format comprises a smartphone application for the patient and a web portal for the clinician to assess, triage and monitor patients remotely. The items have been shown to span all the components of the WHO ICF Framework for health condition
The COVID‐19 Yorkshire Rehabilitation Scale (C19‐YRS): application and psychometric analysis in a post‐COVID‐19 syndrome cohort
As our understanding of the nature and prevalence of post-coronavirus disease 2019 (COVID-19) syndrome (PCS) is increasing, a measure of the impact of COVID-19 could provide valuable insights into patients' perceptions in clinical trials and epidemiological studies as well as routine clinical practice. To evaluate the clinical usefulness and psychometric properties of the COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) in patients with PCS, a prospective, observational study of 187 consecutive patients attending a post-COVID-19 rehabilitation clinic was conducted. The C19-YRS was used to record patients' symptoms, functioning, and disability. A global health question was used to measure the overall impact of PCS on health. Classical psychometric methods (data quality, scaling assumptions, targeting, reliability, and validity) were used to assess the C19-YRS. For the total group, missing data were low, scaling and targeting assumptions were satisfied, and internal consistency was high (Cronbach's α = 0.891). Relationships between the overall perception of health and patients' reports of symptoms, functioning, and disability demonstrated good concordance. This is the first study to examine the psychometric properties of an outcome measure in patients with PCS. In this sample of patients, the C19-YRS was clinically useful and satisfied standard psychometric criteria, providing preliminary evidence of its suitability as a measure of PCS
The modified COVID-19 Yorkshire Rehabilitation Scale (C19-YRSm) patient-reported outcome measure for Long Covid or Post-COVID-19 syndrome
Background
The C19-YRS is the literature's first condition-specific, validated scale for patient assessment and monitoring in Post-COVID-19 syndrome (PCS). The 22-item scale's subscales (scores) are symptom severity (0–100), functional disability (0–50), additional symptoms (0–60), and overall health (0–10).
Objectives
This study aimed to test the scale's psychometric properties using Rasch analysis and modify the scale based on analysis findings, emerging information on essential PCS symptoms, and feedback from a working group of patients and professionals.
Methods
Data from 370 PCS patients were assessed using a Rasch Measurement Theory framework to test model fit, local dependency, response category functioning, differential item functioning, targeting, reliability, and unidimensionality. The working group undertook iterative changes to the scale based on the psychometric results and including essential symptoms.
Results
Symptom severity and functional disability subscales showed good targeting and reliability. Post hoc rescoring suggested that a 4-point response category structure would be more appropriate than an 11-point response for both subscales. Symptoms with binary responses were placed in the other symptoms subscale. The overall health single-item subscale remained unchanged.
Conclusion
A 17-item C19-YRSm was developed with subscales (scores): symptom severity (0–30), functional disability (0–15), other symptoms (0–25), and overall health (0–10)
Turning high-throughput structural biology into predictive inhibitor design
A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein–ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein–ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (MPro), obtaining parallel measurements of over 200 protein–ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry
A Dynamical Systems Model for Combinatorial Cancer Therapy Enhances Oncolytic Adenovirus Efficacy by MEK-Inhibition
Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced or metastatic cases where treatment options remain limited.National Institutes of Health (U.S.) (Grant R01 CA118545)National Institutes of Health (U.S.) (Grant R01 CA095701)National Institutes of Health (U.S.) (Grant U54 CA11297)National Institutes of Health (U.S.) (Grant U54-CA112967
Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study
BACKGROUND: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations.
METHODS: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity.
RESULTS: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32-1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70-6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83-12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63-3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20-2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66-3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89-22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status.
CONCLUSIONS: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients.
FUNDING: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication.
CLINICAL TRIAL NUMBER: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701
Potential Usefulness of Baculovirus-Mediated Sodium-Iodide Symporter Reporter Gene as Non-Invasively Gene Therapy Monitoring in Liver Cancer Cells: An In Vitro
Transcriptomics-Aided Dissection of the Intracellular and Extracellular Roles of Microcystin in Microcystis aeruginosa PCC 7806
Transcriptomics-Aided Dissection of the Intracellular and Extracellular Roles of Microcystin in Microcystis aeruginosa PCC 7806
Recent studies have provided evidence for both intracellular and extracellular roles of the potent hepatotoxin microcystin (MC) in the bloom-forming cyanobacterium Microcystis. Here, we surveyed transcriptomes of the wild-type strain M. aeruginosa PCC 7806 and the microcystin-deficient ΔmcyB mutant under low light conditions with and without the addition of external MC of the LR variant (MC-LR). Transcriptomic data acquired by microarray and quantitative PCR revealed substantial differences in the relative expression of genes of the central intermediary metabolism, photosynthesis, and energy metabolism. In particular, the data provide evidence for a lower photosystem I (PSI)-to-photosystem II (PSII) ratio and a more pronounced carbon limitation in the microcystin-deficient mutant. Interestingly, only 6% of the transcriptional differences could be complemented by external microcystin-LR addition. This MC signaling effect was seen exclusively for genes of the secondary metabolism category. The orphan polyketide synthase gene cluster IPF38-51 was specifically downregulated in response to external MC-LR under low light. Our data suggest a hierarchical and light-dependent cross talk of secondary metabolites and support both an intracellular and an extracellular role of MC in Microcystis
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