Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics analysis.

Metabolomics and foodomics shed light on the molecular processes within living organisms and the complex food composition by leveraging sophisticated analytical techniques to systematically analyze the vast array of molecular features. The traditional feature-picking method often results in arbitrary selections of the model, feature ranking, and cut-off, which may lead to suboptimal results. Thus, a Multiple and Optimal Screening Subset (MOSS) approach was developed in this study to achieve a balance between a minimal number of predictors and high predictive accuracy during statistical model setup. The MOSS approach compares five commonly used models in the context of food matrix analysis, specifically bourbons. These models include Student's t-test, receiver operating characteristic curve, partial least squares-discriminant analysis (PLS-DA), random forests, and support vector machines. The approach employs cross-validation to identify promising subset feature candidates that contribute to food characteristic classification. It then determines the optimal subset size by comparing it to the corresponding top-ranked features. Finally, it selects the optimal feature subset by traversing all possible feature candidate combinations. By utilizing MOSS approach to analyze 1406 mass spectral features from a collection of 122 bourbon samples, we were able to generate a subset of features for bourbon age prediction with 88% accuracy. Additionally, MOSS increased the area under the curve performance of sweetness prediction to 0.898 with only four predictors compared with the top-ranked four features at 0.681 based on the PLS-DA model. Overall, we demonstrated that MOSS provides an efficient and effective approach for selecting optimal features compared with other frequently utilized methods.

Optimal screening schedules for disease progression with application to diabetic retinopathy.

Clinical management of chronic diseases requires periodic evaluations. Subjects transition between various levels of severity of a disease over time, one of which may trigger an intervention that requires treatment. For example, in diabetic retinopathy, patients with type 1 diabetes are evaluated yearly for either the onset of proliferative diabetic retinopathy (PDR) or clinically significant macular edema (CSME) that would require immediate treatment to preserve vision. Herein, we investigate methods for the selection of personalized cost-effective screening schedules and compare them with a fixed visit schedule (e.g., annually) in terms of both cost and performance. The approach is illustrated using the progression of retinopathy in the DCCT/EDIC study.

Optimal decision-making in high-throughput virtual screening pipelines.

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the enormous search space containing the candidates and the substantial computational cost of high-fidelity property prediction models make screening practically challenging. In this work, we propose a general framework for constructing and optimizing a high-throughput virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return on computational investment. Based on both simulated and real-world data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate virtual screening without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.

Can Physical Therapy Deliver Clinically Meaningful Improvements in Pain and Function Through a Mobile App? An Observational Retrospective Study.

Objective: To examine the effect of digital physical therapy (PT) delivered by mobile application (app) on reducing pain and improving function for people with a variety of musculoskeletal conditions. Design: An observational, longitudinal, retrospective study using survey data collected pre- and postdigital PT to estimate multilevel models with random intercepts for patient episodes. Setting: Privately insured employees participating in app-based PT as an employer health care benefit. Participants: The study sample included 814 participants (N=814) 18 years or older who completed their digital PT program with reported final clinical outcomes between February 2019 (program launch) through December 2020. Mean age of the sample at baseline was 40.9±11.89 years, 47.5% were female, 21% sought care for lower back pain, 16% for shoulders, 15% for knees, and 13% for neck. Interventions: Digital PT consisted of a synchronous video evaluation with a physical therapist followed by a course of PT delivered through a mobile app. Main Outcome Measures: Pain was measured by the visual analog scale from 0 "no pain" to 10 "worst pain imaginable" and physical function by the Patient-Specific Functional Scale on a scale from 0 "completely unable to perform" to 10 "able to perform normally." Results: After controlling for significant demographics, comorbid conditions, adverse symptoms, chronicity, and severity, the results from multilevel random intercept models showed decreased pain (-2.69 points; 95% CI, -2.86 to -2.53; P<.001) and increased physical function (+2.67 points; 95% CI, 2.45-2.89; P<.001) after treatment. Conclusions: Digital PT was associated with clinically meaningful improvements in pain and function among a diverse set of participants. These early data are an encouraging indicator of the clinical benefit of digital PT.

Comprehensive Study of Atorvastatin Nanostructured Lipid Carriers through Multivariate Conceptualization and Optimization.

The aim of the current study is to establish a comprehensive experimental design for the screening and optimization of Atorvastatin-loaded nanostructured lipid carriers (AT-NLCs). Initially, combined D-optimal screening design was applied to find the most significant factors affecting AT-NLCs properties. The studied variables included mixtures of solid and liquid lipids, the solid/liquid lipid ratio, surfactant type and concentration, homogenization speed as well as sonication time. Then, the variables homogenization speed (A), the ratio of solid lipid/liquid lipid (B), and concentration of the surfactant (C) were optimized using a central composite design. Particle size, polydispersity index, zeta potential, and entrapment efficiency were chosen as dependent responses. The optimized AT-NLCs demonstrated a nanometric size (83.80 ± 1.13 nm), Polydispersity Index (0.38 ± 0.02), surface charge (-29.65 ± 0.65 mV), and high drug incorporation (93.1 ± 0.04%). Fourier Transform Infrared Spectroscopy (FTIR) analysis showed no chemical interaction between Atorvastatin and the lipid mixture. Differential Scanning Calorimetry (DSC) analysis of the AT-NLCs suggested the transformation of Atorvastatin crystal into an amorphous state. Administration of the optimized AT-NLCs led to a significant reduction (p < 0.001) in serum levels of rats' total cholesterol, triglycerides, and low-density lipoproteins. This change was histologically validated by reducing the relevant steatosis of the liver.