pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of the Penetration Process of Fluorescent Collagenase Nanocapsules in a 3D Collagen Gel
Authors: Victor M. Moreno, Alejandro Baeza, Maria Vallet-Regi
Date: 2021-02-10 00:00:00

One of the major limitations of nanomedicine is the scarce penetration of nanoparticles in tumoral tissues. These constrains have been tried to be solved by different strategies, such as the employ of polyethyleneglycol (PEG) to avoid the opsonization or reducing the extracellular matrix (ECM) density. Our research group has developed some strategies to overcome these limitations such as the employ of pH-sensitive collagenase nanocapsules for the digestion of the collagen-rich extracellular matrix present in most of tumoral tissues. However, a deeper understanding of physicochemical kinetics involved in the nanocapsules degradation process is needed to understand the nanocapsule framework degradation process produced during the penetration in the tissue. For this, in this work it has been employed a double-fluorescent labelling strategy of the polymeric enzyme nanocapsule as a crucial chemical tool which allowed the analysis of nanocapsules and free collagenase during the diffusion process throughout a tumour-like collagen matrix. This extrinsic label strategy provides far greater advantages for observing biological processes. For the detection of enzyme, collagenase has been labelled with fluorescein Isothiocyanate (FITC), whereas the nanocapsule surface was labelled with rhodamine Isothiocyanate (RITC). Thus, it has been possible to monitor the hydrolysis of nanocapsules and their diffusion throughout a thick 3D Collagen gel during the time, obtaining a detailed temporal evaluation of the pH-sensitive collagenase nanocapsule behaviour. These collagenase nanocapsules displayed a high enzymatic activity in low concentrations at acidic pH, and their efficiency to penetrate into tissue models pave the way to a wide range of possible nanomedical applications, especially in cancer therapy.
From sleep medicine to medicine during sleep: A clinical perspective
Authors: Nitai Bar, Jonathan A. Sobel, Thomas Penzel, Yosi Shamay, Joachim A. Behar
Date: 2021-02-09 00:00:00
Tasks: Management, Time Series, Time Series Analysis

Sleep has a profound influence on the physiology of body systems and biological processes. Molecular studies have shown circadian-regulated shifts in protein expression patterns across human tissues, further emphasizing the unique functional, behavioral and pharmacokinetic landscape of sleep. Thus, many pathological processes are also expected to exhibit sleep-specific manifestations. Nevertheless, sleep is seldom utilized for the study, detection and treatment of non-sleep-specific pathologies. Modern advances in biosensor technologies have enabled remote, non-invasive recording of a growing number of physiologic parameters and biomarkers. Sleep is an ideal time frame for the collection of long and clean physiological time series data which can then be analyzed using data-driven algorithms such as deep learning. In this perspective paper, we aim to highlight the potential of sleep as an auspicious time for diagnosis, management and therapy of nonsleep-specific pathologies. We introduce key clinical studies in selected medical fields, which leveraged novel technologies and the advantageous period of sleep to diagnose, monitor and treat pathologies. We then discuss possible opportunities to further harness this new paradigm and modern technologies to explore human health and disease during sleep and to advance the development of novel clinical applications: From sleep medicine to medicine during sleep.
What should patients do if they miss a dose of medication? A theoretical approach
Authors: Elijah D Counterman, Sean D Lawley
Date: 2021-02-09 00:00:00

Medication adherence is a major problem for patients with chronic diseases that require long term pharmacotherapy. Many unanswered questions surround adherence, including how adherence rates translate into treatment efficacy and how missed doses of medication should be handled. To address these questions, we formulate and analyze a mathematical model of the drug concentration in a patient with imperfect adherence. We find exact formulas for drug concentration statistics, including the mean, the coefficient of variation, and the deviation from perfect adherence. We determine how adherence rates translate into drug concentrations, and how this depends on the drug half-life, the dosing interval, and how missed doses are handled. While clinical recommendations require extensive validation and should depend on drug and patient specifics, as a general principle our theory suggests that nonadherence is best mitigated by taking double doses following missed doses if the drug has a long half-life. This conclusion contradicts some existing recommendations that cite long drug half-lives as the reason to avoid a double dose after a missed dose. Furthermore, we show that a patient who takes double doses after missed doses can have at most only slightly more drug in their body than a perfectly adherent patient if the drug half-life is long. We also investigate other ways of handling missed doses, including taking an extra fractional dose following a missed dose. We discuss our results in the context of hypothyroid patients taking levothyroxine.
cgmquantify: Python and R packages for comprehensive analysis of interstitial glucose and glycemic variability from continuous glucose monitor data
Authors: Brinnae Bent, Maria Henriquez, Jessilyn Dunn
Date: 2021-02-08 00:00:00
Tasks: Management

Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day (typically values are recorded every 5 minutes). CGMs are commonly used in diabetes management by clinicians and patients and in research to understand how factors of longitudinal glucose and glucose variability relate to disease onset and severity and the efficacy of interventions. CGM data presents unique bioinformatic challenges because the data is longitudinal, temporal, and there are nearly infinite possible ways to summarize and use this data. There are over 20 metrics of glucose variability, no standardization of metrics, and little validation across studies. Here we present open source python and R packages called cgmquantify, which contains over 20 functions with over 25 clinically validated metrics of glucose and glucose variability and functions for visualizing longitudinal CGM data. This is expected to be useful for researchers and may provide additional insights to patients and clinicians about glucose patterns.
Interval Analysis of Worst-case Stationary Moments for Stochastic Chemical Reactions with Uncertain Parameters
Authors: Yuta Sakurai, Yutaka Hori
Date: 2021-02-08 00:00:00

The dynamics of cellular chemical reactions are variable due to stochastic noise from intrinsic and extrinsic sources. The intrinsic noise is the intracellular fluctuations of molecular copy numbers caused by the probabilistic encounter of molecules and is modeled by the chemical master equation. The extrinsic noise, on the other hand, represents the intercellular variation of the kinetic parameters due to the variation of global factors affecting gene expression. The objective of this paper is to propose a theoretical framework to analyze the combined effect of the intrinsic and the extrinsic noise modeled by the chemical master equation with uncertain parameters. More specifically, we formulate a semidefinite program to compute the intervals of the stationary solution of uncertain moment equations whose parameters are given only partially in the form of the statistics of their distributions. The semidefinite program is derived without approximating the governing equation in contrast with many existing approaches. Thus, we can obtain guaranteed intervals of the worst possible values of the moments for all parameter distributions satisfying the given statistics, which are prohibitively hard to estimate from sample-path simulations since sampling from all possible uncertain distributions is difficult. We demonstrate the proposed optimization approach using two examples of stochastic chemical reactions and show that the solution of the optimization problem gives informative upper and lower bounds of the statistics of the stationary copy number distributions.
Fine-tuning neural excitation/inhibition for tailored ketamine use in treatment-resistant depression
Authors: Erik D. Fagerholm, Robert Leech, Steven Williams, Carlos A. Zarate Jr., Rosalyn J. Moran, Jessica R. Gilbert
Date: 2021-02-04 00:00:00

The glutamatergic modulator ketamine has been shown to rapidly reduce depressive symptoms in patients with treatment-resistant major depressive disorder (TRD). Although its mechanisms of action are not fully understood, changes in cortical excitation/inhibition (E/I) following ketamine administration are well documented in animal models and could represent a potential biomarker of treatment response. Here, we analyse neuromagnetic virtual electrode timeseries collected from the primary somatosensory cortex in 18 unmedicated patients with TRD and in an equal number of age-matched healthy controls during a somatosensory 'airpuff' stimulation task. These two groups were scanned as part of a clinical trial of ketamine efficacy under three conditions: a) baseline; b) 6-9 hours following subanesthetic ketamine infusion; and c) 6-9 hours following placebo-saline infusion. We obtained estimates of E/I interaction strengths by using Dynamic Causal Modelling (DCM) on the timeseries, thereby allowing us to pinpoint, under each scanning condition, where each subject's dynamics lie within the Poincar\'e diagram - as defined in dynamical systems theory. We demonstrate that the Poincar\'e diagram offers classification capability for TRD patients, in that the further the patients' coordinates were shifted (by virtue of ketamine) toward the stable (top-left) quadrant of the Poincar\'e diagram, the more their depressive symptoms improved. The same relationship was not observed by virtue of a placebo effect - thereby verifying the drug-specific nature of the results. We show that the shift in neural dynamics required for symptom improvement necessitates an increase in both excitatory and inhibitory coupling. We present accompanying MATLAB code made available in a public repository, thereby allowing for future studies to assess individually-tailored treatments of TRD.
Rate-dependent effects of lidocaine on cardiac dynamics: Development and analysis of a low-dimensional drug-channel interaction model
Authors: Steffen S. Docken, Colleen E. Clancy, Timothy J. Lewis
Date: 2021-02-03 00:00:00

State-dependent Na+ channel blockers are often prescribed to treat cardiac arrhythmias, but many Na+ channel blockers are known to have pro-arrhythmic side effects. While the anti and proarrhythmic potential of a Na+ channel blocker is thought to depend on the characteristics of its rate-dependent block, the mechanisms linking these two attributes are unclear. Furthermore, how specific properties of rate-dependent block arise from the binding kinetics of a particular drug is poorly understood. Here, we examine the rate-dependent effects of the Na+ channel blocker lidocaine by constructing and analyzing a novel drug-channel interaction model. First, we identify the predominant mode of lidocaine binding in a 24 variable Markov model for lidocaine-Na+ channel interaction by Moreno et al. We then develop a novel 3-variable lidocaine-Na+ channel interaction model that incorporates only the predominant mode of drug binding. Our low-dimensional model replicates the extensive voltage-clamp data used to parameterize the Moreno et al. model. Furthermore, the effects of lidocaine on action potential upstroke velocity and conduction velocity in our model are similar to those predicted by the Moreno et al. model. By exploiting the low-dimensionality of our model, we derive an algebraic expression for level of rate-dependent block as a function of pacing frequency, restitution properties, diastolic and plateau potentials, and drug binding rate constants. Our model predicts that the level of rate-dependent block is sensitive to alterations in restitution properties and increases in diastolic potential, but it is insensitive to variations in the shape of the action potential waveform and lidocaine binding rates.
Dietary Supplements and Nutraceuticals Under Investigation for COVID-19 Prevention and Treatment
Authors: Ronan Lordan, Halie M. Rando, COVID-19 Review Consortium, Casey S. Greene
Date: 2021-02-03 00:00:00

Coronavirus disease 2019 (COVID-19) has caused global disruption and a significant loss of life. Existing treatments that can be repurposed as prophylactic and therapeutic agents could reduce the pandemic's devastation. Emerging evidence of potential applications in other therapeutic contexts has led to the investigation of dietary supplements and nutraceuticals for COVID-19. Such products include vitamin C, vitamin D, omega 3 polyunsaturated fatty acids, probiotics, and zinc, all of which are currently under clinical investigation. In this review, we critically appraise the evidence surrounding dietary supplements and nutraceuticals for the prophylaxis and treatment of COVID-19. Overall, further study is required before evidence-based recommendations can be formulated, but nutritional status plays a significant role in patient outcomes, and these products could help alleviate deficiencies. For example, evidence indicates that vitamin D deficiency may be associated with greater incidence of infection and severity of COVID-19, suggesting that vitamin D supplementation may hold prophylactic or therapeutic value. A growing number of scientific organizations are now considering recommending vitamin D supplementation to those at high risk of COVID-19. Because research in vitamin D and other nutraceuticals and supplements is preliminary, here we evaluate the extent to which these nutraceutical and dietary supplements hold potential in the COVID-19 crisis.
Effect of stress on cardiorespiratory synchronization of Ironmen athletes
Authors: Maia Angelova, Philip M. Holloway, Sergiy Shelyag, Sutharshan Rajasegarar, H. G. Laurie Rauch
Date: 2021-02-03 00:00:00

The aim of this paper is to investigate the cardiorespiratory synchronization in athletes subjected to extreme physical stress combined with a cognitive stress tasks. ECG and respiration were measured in 14 athletes before and after the Ironmen competition. Stroop test was applied between the measurements before and after the Ironmen competition to induce cognitive stress. Synchrogram and empirical mode decomposition analysis were used for the first time to investigate the effects of physical stress, induced by the Ironmen competition, on the phase synchronization of the cardiac and respiratory systems of Ironmen athletes before and after the competition. A cognitive stress task (Stroop test) was performed both pre- and post-Ironman event in order to prevent the athletes from cognitively controlling their breathing rates. Our analysis showed that cardiorespiratory synchronization increased post-Ironman race compared to pre-Ironman. The results suggest that the amount of stress the athletes are recovering from post-competition is greater than the effects of the Stroop test. This indicates that the recovery phase after the competition is more important for restoring and maintaining homeostasis, which could be another reason for stronger synchronization.
Biomembranes undergo complex, non-axisymmetric deformations governed by Kirchhoff-Love kinematics and revealed by a three dimensional computational framework
Authors: Debabrata Auddya, Xiaoxuan Zhang, Rahul Gulati, Ritvik Vasan, Krishna Garikipati, Padmini Rangamani, Shiva Rudraraju
Date: 2021-01-28 00:00:00

Biomembranes play a central role in various phenomena like locomotion of cells, cell-cell interactions, packaging of nutrients, and in maintaining organelle morphology and functionality. During these processes, the membranes undergo significant morphological changes through deformation, scission, and fusion. Modeling the underlying mechanics of such morphological changes has traditionally relied on reduced order axisymmetric representations of membrane geometry and deformation. Axisymmetric representations, while robust and extensively deployed, suffer from their inability to model symmetry breaking deformations and structural bifurcations. To address this limitation, a 3D computational mechanics framework for high fidelity modeling of biomembrane deformation is presented. The proposed framework brings together Kirchhoff-Love thin-shell kinematics, Helfrich-energy based mechanics, and state-of-the-art numerical techniques for modeling deformation of surface geometries. Lipid bilayers are represented as spline-based surfaces immersed in a 3D space; this enables modeling of a wide spectrum of membrane geometries, boundary conditions, and deformations that are physically admissible in a 3D space. The mathematical basis of the framework and its numerical machinery are presented, and their utility is demonstrated by modeling 3 classical, yet non-trivial, membrane problems: formation of tubular shapes and their lateral constriction, Piezo1-induced membrane footprint generation and gating response, and the budding of membranes by protein coats during endocytosis. For each problem, the full 3D membrane deformation is captured, potential symmetry-breaking deformation paths identified, and various case studies of boundary and load conditions are presented. Using the endocytic vesicle budding as a case study, we also present a "phase diagram" for its symmetric and broken-symmetry states.
Injury risk increases minimally over a large range of changes in activity level in children
Authors: Chinchin Wang, Tyrel Stokes, jorge Trejo Vargas, Russell Steele, Niels Wedderkopp, Ian Shrier
Date: 2020-10-06 00:00:00
Tasks: Additive models

Background: Limited research exists on the association between changes in physical activity levels and injury in children. Objective: To assess how well different variations of the acute:chronic workload ratio (ACWR), a measure of change in activity, predict injury in children. Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years (2008 to 2014). Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load (numerator), and average weekly activity frequency in the past 4 or 5 weeks as the chronic load (denominator). We modelled the relationship between different ACWR variations and injury using generalized linear and generalized additive models, with and without accounting for repeated measures. Results: The prognostic relationship between the ACWR and injury risk was best represented using a generalized additive mixed model for the uncoupled 5-week ACWR. It predicted an injury risk of ~3% for ACWRs between 0.8 (activity level decreased by 20%) and 1.5 (activity level increased by 50%). When activity decreased by more than 20% (ACWR< 0.8), injury risk was lower (minimum of 1.5% at ACWR=0). When activity increased by more than 50% (ACWR > 1.5), injury risk was higher (maximum of 6% at ACWR = 5). Girls were at significantly higher risk of injury than boys. Conclusion: Increases in physical activity in children are associated with much lower injury risks compared to previous results in adults.
Automating LC-MS/MS mass chromatogram quantification. Wavelet transform based peak detection and automated estimation of peak boundaries and signal-to-noise ratio using signal processing methods
Authors: Florian Rupprecht, Sören Enge, Kornelius Schmidt, Wei Gao, Clemens Kirschbaum, Robert Miller
Date: 2021-01-21 00:00:00
Tasks: Diagnostic, valid

While there are many different methods for peak detection, no automatic methods for marking peak boundaries to calculate area under the curve (AUC) and signal-to-noise ratio (SNR) estimation exist. An algorithm for the automation of liquid chromatography tandem mass spectrometry (LC-MS/MS) mass chromatogram quantification was developed and validated. Continuous wavelet transformation and other digital signal processing methods were used in a multi-step procedure to calculate concentrations of six different analytes. To evaluate the performance of the algorithm, the results of the manual quantification of 446 hair samples with 6 different steroid hormones by two experts were compared to the algorithm results. The proposed approach of automating mass chromatogram quantification is reliable and valid. The algorithm returns less nondetectables than human raters. Based on signal to noise ratio, human non-detectables could be correctly classified with a diagnostic performance of AUC = 0.95. The algorithm presented here allows fast, automated, reliable, and valid computational peak detection and quantification in LC- MS/MS.
Fractal Dimension and Retinal Pathology: A Meta-analysis
Authors: Sam Yu, Vasudevan Lakshminarayanan
Date: 2021-01-21 00:00:00
Tasks: Articles, Diagnostic

Due to the fractal nature of retinal blood vessels, the retinal fractal dimension is a natural parameter for researchers to explore and has garnered interest as a potential diagnostic tool. This review aims to summarize the current scientific evidence regarding the relationship between fractal dimension and retinal pathology and thus assess the clinical value of retinal fractal dimension. Following the PRISMA guidelines, a literature search for research articles was conducted in several internet databases (Embase, PubMed, Web of Science, Scopus). This led to a result of 28 studies included in the final review, which were analyzed via meta-analysis to determine whether the fractal dimension changes significantly in retinal disease versus normal individuals
Automatic prediction of cognitive and functional decline can significantly decrease the number of subjects required for clinical trials in early Alzheimer's disease
Authors: Neda Shafiee, Mahsa Dadar, Simon Ducharme, D. Louis Collins
Date: 2021-01-20 00:00:00
Tasks: Sensitivity, Specificity

INTRODUCTION: Heterogeneity in the progression of Alzheimer's disease makes it challenging to predict the rate of cognitive and functional decline for individual patients. Tools for short-term prediction could help enrich clinical trial designs and focus prevention strategies on the most at-risk patients. METHOD: We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects with mild cognitive impairment remained stable and which functionally declined (measured by a two-point increase in CDR-SB) over 2 and 3-year follow-up periods, periods typical of the length of clinical trials. RESULTS: Combining both sets of features yields 77% accuracy (81% sensitivity and 75% specificity) to predict cognitive decline at 2 years (74% accuracy at 3 years with 75% sensitivity and 73% specificity). Using this tool to select trial participants yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25% treatment effect to reduce cognitive decline. DISCUSSION: This cohort enrichment tool could accelerate treatment development by increasing power in clinical trials.