Raymcmahon8388

P-glycoprotein (P-gp) is an efflux pump implicated in pharmacokinetics and drug-drug interactions. The identification of its substrates is consequently an important issue, notably for drugs under development. For such a purpose, various in silico methods have been developed, but their relevance remains to be fully established. The present study was designed to get insight about this point, through determining the performance values of six freely accessible Web-tools (ADMETlab, AdmetSAR2.0, PgpRules, pkCSM, SwissADME and vNN-ADMET), computationally predicting P-gp-mediated transport. Using an external test set of 231 marketed drugs, approved over the 2010-2020 period by the US Food and Drug Administration and fully in vitro characterized for their P-gp substrate status, various performance parameters (including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the receiver operating characteristics curve) were determined. They were found to rather poorly meet criteria commonly required for acceptable prediction, whatever the Web-tools were used alone or in combination. Predictions of being P-gp substrate or non-substrate by these online in silico methods may therefore be considered with caution.

Spectral CT uses energy-dependent measurements that enable material discrimination in addition to reconstruction of structural information. Flat-panel detectors (FPDs) have been widely used in dedicated and interventional systems to deliver high spatial resolution, volumetric cone-beam CT (CBCT) in compact and OR-friendly designs. In this work, we derive a model-based method that facilitates high-resolution material decomposition in a spectral CBCT system equipped with a prototype dual-layer FPD. Through high-fidelity modeling of multilayer detector, we seek to avoid resolution loss that is present in more traditional processing and decomposition approaches.

A physical model for spectral measurements in dual-layer flat-panel CBCT is developed including layer-dependent differences in system geometry, spectral sensitivities, and detector blur (e.g., due to varied scintillator thicknesses). This forward model is integrated into a model-based material decomposition (MBMD) method based on minimization of a penthe potential to not only facilitate high-resolution spectral CT in interventional and dedicated CBCT systems, but may also provide the opportunity to evaluate different flat-panel design trade-offs including multilayer FPDs with mismatched geometries, scintillator thicknesses, and spectral sensitivities.Correctly and quickly identifying disease patterns and clusters is a vital aspect of public health and epidemiology so that disease outbreaks can be mitigated as effectively as possible. The circular scan method is one of the most commonly used methods for detecting disease outbreaks and clusters in retrospective and prospective disease surveillance. The circular scan method requires a population upper bound in order to construct the set of candidate zones to be scanned, which is usually set to 50% of the total population. The performance of the circular scan method is affected by the choice of the population upper bound, and choosing an upper bound different from the default value can improve the method's performance. Recently, the Gini coefficient based on the Lorenz curve, which was originally used in economics, was proposed to determine a better population upper bound. We present the elbow method, a new method for choosing the population upper bound, which seeks to address some of the limitations of the Gini-based method while improving the performance of the circular scan method over the default value. To evaluate the performance of the proposed approach, we evaluate the sensitivity and positive predictive value of the circular scan method for publicly-available benchmark data for the default value, the Gini coefficient method, and the elbow method.

The physical exam component of a periodic health visit in the elderly has not been considered useful. Standard Medicare Wellness visits require no physical exam beyond blood pressure and most physicians perform limited exams during these visits. check details The objective of this study was to test the feasibility, potential benefit, and costs of performing a screening ultrasound (US) exam during Medicare Wellness visits.

A physician examiner at an academic internal medicine primary care clinic performed a screening US exam targeting important abnormalities of patients 65-85 years old during a Medicare Wellness visit. The primary care physician (PCP) recorded the follow-up items for each abnormality identified by the US examiner and assessed the benefit of each abnormality for the participant. Abnormality benefit, net exam benefit per participant, follow-up items and costs, participant survey results, and exam duration were assessed.

Participants numbered 108. Total abnormalities numbered 283 and new diagnoses were 172. Positive benefit scores were assigned to 38.8%, neutral (zero) scores to 59.4%, and negative benefit scores to 1.8% of abnormalities. Net benefit scores per participant were positive in 63.9%, 0 in 34.3%, and negative in 1.8%. Follow-up items were infrequent resulting in 76% of participants without follow-up cost. Participant survey showed excellent acceptance of the exam.

The US screening exam identified frequent abnormalities in Medicare Wellness patients. The assessed benefits were rarely negative and often mild to moderately positive, with important new chronic conditions identified. Follow-up costs were low when the PCPs were also US experts.

The US screening exam identified frequent abnormalities in Medicare Wellness patients. The assessed benefits were rarely negative and often mild to moderately positive, with important new chronic conditions identified. Follow-up costs were low when the PCPs were also US experts.In biomedical studies it is common to collect data on multiple biomarkers during study follow-up for dynamic prediction of a time-to-event clinical outcome. The biomarkers are typically intermittently measured, missing at some event times, and may be subject to high biological variations, which cannot be readily used as time-dependent covariates in a standard time-to-event model. Moreover, they can be highly correlated if they are from in the same biological pathway. To address these issues, we propose a flexible joint model framework that models the multiple biomarkers with a shared latent reduced rank longitudinal principal component model and correlates the latent process to the event time by the Cox model for dynamic prediction of the event time. The proposed joint model for highly correlated biomarkers is more flexible than some existing methods since the latent trajectory shared by the multiple biomarkers does not require specification of a priori parametric time trend and is determined by data. We derive an expectation-maximization (EM) algorithm for parameter estimation, study large sample properties of the estimators, and adapt the developed method to make dynamic prediction of the time-to-event outcome.