Reductions in levels of the hunger-stimulating hormone ghrelin have been proposed to mediate part of the effects of vertical sleeve gastrectomy (VSG) and Roux-en-Y gastric bypass surgeries for ...obesity. We studied circulating levels of acyl and desacyl ghrelin in rats after these surgeries. We found that blood levels of ghrelin were reduced after VSG, but not after Roux-en-Y gastric bypass, based on enzyme-linked immunosorbent assay and mass-spectrometry analyses. We compared the effects of VSG in ghrelin-deficient mice and wild-type mice on food intake, body weight, dietary fat preference, and glucose tolerance. We found that VSG produced comparable outcomes in each strain. Reduced ghrelin signaling therefore does not appear to be required for these effects of VSG.
Influenza viruses are globally important human respiratory pathogens. These viruses cause seasonal epidemics and occasional worldwide pandemics, both of which can vary significantly in disease ...severity. The virulence of a particular influenza virus strain is partly determined by its success in circumventing the host immune response. This article briefly reviews the innate mechanisms that host cells have evolved to resist virus infection, and outlines the plethora of strategies that influenza viruses have developed in order to counteract such powerful defences. The molecular details of this virus-host interplay are summarized, and the ways in which research in this area is being applied to the rational design of protective vaccines and novel antivirals are discussed.
Abstract
Objectives
To assess fairness and bias of a previously validated machine learning opioid misuse classifier.
Materials & Methods
Two experiments were conducted with the classifier’s original ...(n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier’s predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics.
Results
We identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included “heroin” and “substance abuse” across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05).
Discussion
The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed.
Conclusion
Standardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.
Fibroblast Growth Factor 21 Reverses Hepatic Steatosis, Increases Energy Expenditure, and Improves Insulin Sensitivity in
Diet-Induced Obese Mice
Jing Xu 1 ,
David J. Lloyd 1 ,
Clarence Hale 1 ,
...Shanaka Stanislaus 1 ,
Michelle Chen 1 ,
Glenn Sivits 1 ,
Steven Vonderfecht 2 ,
Randy Hecht 3 ,
Yue-Sheng Li 3 ,
Richard A. Lindberg 1 ,
Jin-Long Chen 1 ,
Dae Young Jung 4 ,
Zhiyou Zhang 4 ,
Hwi-Jin Ko 4 ,
Jason K. Kim 4 and
Murielle M. Véniant 1
1 Department of Metabolic Disorders, Amgen, Thousand Oaks, California
2 Department of Pathology, Amgen, Thousand Oaks, California
3 Department of Protein Sciences, Amgen, Thousand Oaks, California
4 Department of Cellular and Molecular Physiology, Pennsylvania State University College of Medicine, Hershey, Pennsylvania
Corresponding author: Jing Xu, jingx{at}amgen.com
Abstract
OBJECTIVE— Fibroblast growth factor 21 (FGF21) has emerged as an important metabolic regulator of glucose and lipid metabolism. The aims
of the current study are to evaluate the role of FGF21 in energy metabolism and to provide mechanistic insights into its glucose
and lipid-lowering effects in a high-fat diet–induced obesity (DIO) model.
RESEARCH DESIGN AND METHODS— DIO or normal lean mice were treated with vehicle or recombinant murine FGF21. Metabolic parameters including body weight,
glucose, and lipid levels were monitored, and hepatic gene expression was analyzed. Energy metabolism and insulin sensitivity
were assessed using indirect calorimetry and hyperinsulinemic-euglycemic clamp techniques.
RESULTS— FGF21 dose dependently reduced body weight and whole-body fat mass in DIO mice due to marked increases in total energy expenditure
and physical activity levels. FGF21 also reduced blood glucose, insulin, and lipid levels and reversed hepatic steatosis.
The profound reduction of hepatic triglyceride levels was associated with FGF21 inhibition of nuclear sterol regulatory element
binding protein-1 and the expression of a wide array of genes involved in fatty acid and triglyceride synthesis. FGF21 also
dramatically improved hepatic and peripheral insulin sensitivity in both lean and DIO mice independently of reduction in body
weight and adiposity.
CONCLUSIONS— FGF21 corrects multiple metabolic disorders in DIO mice and has the potential to become a powerful therapeutic to treat hepatic
steatosis, obesity, and type 2 diabetes.
Footnotes
Published ahead of print at http://diabetes.diabetesjournals.org on 7 October 2008.
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work
is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore
be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Accepted September 29, 2008.
Received March 19, 2008.
DIABETES
Background and Aims
Unhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into ...electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center.
Design
Retrospective cohort study.
Setting
The site for validation was a midwestern United States tertiary‐care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service.
Participants/Cases
Unplanned admissions of adult patients between October 23, 2017 and December 31, 2019, with EHR documentation of manual alcohol screening were included in the cohort (n = 57 605).
Measurements
The Alcohol Use Disorders Identification Test (AUDIT) served as the reference standard. AUDIT scores ≥5 for females and ≥8 for males served as cases for UAU. To examine error in manual screening or under‐reporting, a post hoc error analysis was conducted, reviewing discordance between the NLP classifier and AUDIT‐derived reference. All clinical notes excluding the manual screening and AUDIT documentation from the EHR were included in the NLP analysis.
Findings
Using clinical notes from the first 24 hours of each encounter, the NLP classifier demonstrated an area under the receiver operating characteristic curve (AUCROC) and precision‐recall area under the curve (PRAUC) of 0.91 (95% CI = 0.89–0.92) and 0.56 (95% CI = 0.53–0.60), respectively. At the optimal cut point of 0.5, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.66 (95% CI = 0.62–0.69), 0.98 (95% CI = 0.98–0.98), 0.35 (95% CI = 0.33–0.38), and 1.0 (95% CI = 1.0–1.0), respectively.
Conclusions
External validation of a publicly available alcohol misuse classifier demonstrates adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at‐risk patients.
Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured ...electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse.
An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort.
Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99-0.99) across the encounter and 0.98 (95% CI 0.98-0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77-0.84) and 0.72 (95% CI 0.68-0.75). For the first 24 h, they were 0.75 (95% CI 0.71-0.78) and 0.61 (95% CI 0.57-0.64).
Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.
The discovery of novel 4-hydroxy-2-(heterocyclic)pyrimidine-5-carboxamide inhibitors of hypoxia-inducible factor (HIF) prolyl hydroxylases (PHD) is described. These are potent, selective, orally ...bioavailable across several species, and active in stimulating erythropoiesis. Mouse and rat studies showed hematological changes with elevations of plasma EPO and circulating reticulocytes following single oral dose administration, while 4-week q.d. po administration in rat elevated hemoglobin levels. A major focus of the optimization process was to decrease the long half-life observed in higher species with early compounds. These efforts led to the identification of 28 (MK-8617), which has advanced to human clinical trials for anemia.
Here, we explicitly define a half-cell reaction approach for pH calculation using the electrode couple comprised of the solid-state chloride ion-selective electrode (Cl-ISE) as the reference ...electrode and the hydrogen ion-selective ion-sensitive field effect transistor (ISFET) of the Honeywell Durafet as the hydrogen ion H+-sensitive measuring or working electrode. This new approach splits and isolates the independent responses of the Cl-ISE to the chloride ion Cl− (and salinity) and the ISFET to H+ (and pH), and calculates pH directly on the total scale pHtotalEXT in molinity (mol (kg-soln)−1) concentration units. We further apply and compare pHtotalEXT calculated using the half-cell and the existing complete cell reaction (defined by Martz et al. (2010)) approaches using measurements from two SeapHOx sensors deployed in a test tank. Salinity (on the Practical Salinity Scale) and pH oscillated between 1 and 31 and 6.9 and 8.1, respectively, over a six-day period.
In contrast to established Sensor Best Practices, we employ a new calibration method where the calibration of raw pH sensor timeseries are split out as needed according to salinity. When doing this, pHtotalEXT had root-mean squared errors ranging between ±0.0026 and ±0.0168 pH calculated using both reaction approaches relative to pHtotal of the discrete bottle samples pHtotaldisc. Our results further demonstrate the rapid response of the Cl-ISE reference to variable salinity with changes up to ±12 (30 min)−1. Final calculated pHtotalEXT were ≤±0.012 pH when compared to pHtotaldisc following salinity dilution or concentration. These results are notably in contrast to those of the few in situ field deployments over similar environmental conditions that demonstrated pHtotalEXT calculated using the Cl-ISE as the reference electrode had larger uncertainty in nearshore waters. Therefore, additional work beyond the correction of variable temperature and salinity conditions in pH calculation using the Cl-ISE is needed to examine the effects of other external stimuli on in situ electrode response. Furthermore, whereas past work has focused on in situ reference electrode response, greater scrutiny of the ISFET as the H+-sensitive measuring electrode for pH measurement in natural waters is also needed.
•Electrode responses are split out and isolated using half reactions.•A new approach for pH calculation using half reactions is explicitly defined.•pH calculation uses single ion activity coefficients for the chloride and hydrogen ions.•This new pH calculation approach is optimized for salinities between 0.105 and 35.•pH can now be calculated directly on the total scale in molinity (mol (kg soln)−1).
The contagion number: How fast can a disease spread? Blessley, Misty; Davila, Randy; Hale, Trevor ...
Decision Making. Applications in Management and Engineering (Online),
4/2023, Letnik:
6, Številka:
1
Journal Article
Recenzirano
The burning number of a graph models the rate at which a disease, information, or other externality can propagate across a network. The burning number is known to be NP-hard even for a tree. Herein, ...we define a relative of the burning number that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the burning number as it only counts first time infections (i.e., constrains a node from getting the same disease/same variant/same alarm more than once). This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a method to solve for the contagion number of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis) and then employs simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are used to model human social networks generated through a preferential attachment mechanism. With CN differing across network structures and almost identical to ACN, our findings advance disease spread understanding and reveal the importance of network structure. In a borderless world without replete resources, understanding disease spread can do much to inform public policy and managerial decision makers’ allocation decisions. Furthermore, our direct interactions with supply chain executives at two COVID-19 vaccine developers provided practical grounding on what the results suggest for achieving social welfare objectives.
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