biological variability
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2022 ◽  
Author(s):  
Vardges Tserunyan ◽  
Stacey D Finley

In recent decades, chimeric antigen receptors (CARs) have been successfully used to generate engineered T cells capable of recognizing and eliminating cancer cells. The structure of CARs frequently includes costimulatory domains, which enhance the T cell response upon antigen encounter. However, it is not fully known how the CAR co-stimulatory domains influence T cell activation in the presence of biological variability. In this work, we used mathematical modeling to elucidate how the inclusion of one such co-stimulatory molecule, CD28, impacts the response of a population of engineered T cells under different sources of variability. Particularly, our simulations demonstrate that CD28-bearing CARs mediate a faster and more consistent population response under both target antigen variability and kinetic rate variability. We identify kinetic parameters that have the most impact on mediating cell activation. Finally, based on our findings, we propose that enhancing the catalytic activity of lymphocyte-specific protein tyrosine kinase (LCK) can result in drastically reduced and more consistent response times among heterogeneous CAR T cell populations.


2021 ◽  
Vol 57 (2) ◽  
pp. 9-18
Author(s):  
Adrian Martyniak ◽  
Przemysław Tomasik

The results of laboratory tests are analyzed against reference values that are determined in a population of healthy people prepared for the test in accordance with the relevant guidelines. Such a reference system works perfectly when analyzing the results of tests of patients from whom the material for determinations was collected in similar conditions. To better match the reference values ranges are stratified, most often by gender, age, or race of the patient – the most common and the most significant biological variability. The values of the measured parameter are also influenced by within-subject biological variability to e.g. the time of the day, food consumption, or physical exercise. This variability influences the results of random testing, often performed in patients with emergencies. The measure of both of these variations is the index of individuality, i.e. the ratio of within-subject biological variability to between-subject biological variability. In the present work, the factors influencing the circadian, seasonal, and between-subject biological variations of the selected clinical chemistry parameters are presented. Knowledge about these variations is important for the physician and the supporting laboratory diagnostician, particularly helpful in the analysis of pathological or inconsistent with the clinicians' expectations results to distinguish results related to the disease from results related to biological variability.


2021 ◽  
Author(s):  
Gloria Colombo ◽  
Ryan John Abat Cubero ◽  
Lida Kanari ◽  
Alessandro Venturino ◽  
Rouven Schulz ◽  
...  

Microglia contribute to tissue homeostasis in physiological conditions with environmental cues influencing their ever-changing morphology. Strategies to identify these changes usually involve user-selected morphometric features, which, however, have proved ineffective in establishing a spectrum of context-dependent morphological phenotypes. Here, we have developed MorphOMICs, a topological data analysis approach to overcome feature-selection-based biases and biological variability. We extracted a spatially heterogeneous and sexually-dimorphic morphological phenotype for seven adult brain regions, with ovariectomized females forming their own distinct cluster. This sex-specific phenotype declines with maturation but increases over the disease trajectories in two neurodegeneration models, 5xFAD and CK-p25. Females show an earlier morphological shift in the immediately-affected brain regions. Finally, we demonstrate that both the primary- and the short terminal processes provide distinct insights to morphological phenotypes. MorphOMICs maps microglial morphology into a spectrum of cue-dependent phenotypes in a minimally-biased and semi-automatic way.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei Shi ◽  
Menghua Wang

AbstractThe global daily gap-free chlorophyll-a (Chl-a) data derived using the data interpolating empirical orthogonal functions (DINEOF) technique from observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) in 2020 and the in situ measurements at the Tropical Ocean Atmosphere (TAO) moorings are used to characterize and quantify the biological variability modulated by the tropical instability wave (TIW). Our study aims to understand how ocean physical processes are linked to biological variability. In this study, we use the TAO in situ measurements and the coincident VIIRS Chl-a data to identify the mechanism that drives ocean biological variability corresponding to the TIW. Satellite observations show that the TIW-driven Chl-a variability stretched from 90°W to 160°E in the region. The enhanced Chl-a pattern propagated westward and moderately matched the cooler sea surface temperature (SST) patterns in the Equatorial Pacific Ocean. In fact, the Chl-a variation driven by the TIW is about ± 30% of mean Chl-a values. Furthermore, the time series of Chl-a at 140°W along the equator was found to be in phase with sea surface salinity (SSS) at 140°W along the equator at the TAO mooring since late May 2020. The cross-correlation coefficients with the maximum magnitude between Chl-a and SST, Chl-a and SSS, and Chl-a and dynamic height were –0.46, + 0.74, and –0.58, respectively, with the corresponding time lags of about 7 days, 1 day, and 8 days, respectively. The different spatial patterns of the cooler SST and enhanced Chl-a are attributed to the phase difference in Chl-a and SST. Indeed, a Chl-a peak normally coincided with a SSS peak and vice versa. This could be attributed to the consistency in the change in nutrient concentration with respect to the change of SSS. The vertical distributions of the temperature and salinity at 140°W along the equator reveal that the TIW leads to changes in both salinity and nutrient concentrations in the sea surface, and consequently drives the Chl-a variability from late May until the end of the year 2020.


2021 ◽  
Author(s):  
Ross Lawrence ◽  
Alexander Loftus ◽  
Gregory Kiar ◽  
Eric Bridgeford ◽  
Vikram Chandrashekhar ◽  
...  

Connectomics-the study of brain networks-provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used to process MRI data from 35 different studies (~6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that followed established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes generated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://neurodata.io/mri/.


Author(s):  
Marieke Biniasch ◽  
Ruediger Paul Laubender ◽  
Martin Hund ◽  
Katharina Buck ◽  
Christian De Geyter

Abstract Objectives Determine variability of serum anti-Müllerian hormone (AMH) levels during ovulatory menstrual cycles between different women (inter-participant), between non-consecutive cycles (inter-cycle) and within a single cycle (intra-cycle) in healthy women. Methods Eligible participants were women aged 18–40 years with regular ovulatory menstrual cycles. Serum samples were collected every second day during two non-consecutive menstrual cycles. AMH levels were measured in triplicate using the Elecsys® AMH Plus immunoassay (Roche Diagnostics). AMH level variability was evaluated using mixed-effects periodic regression models based on Fourier series. The mesor was calculated to evaluate inter-participant and inter-cycle variability. Inter- and intra-cycle variability was evaluated using peak-to-peak amplitudes. Separation of biological and analytical coefficients of variation (CVs) was determined by analysing two remeasured AMH levels (with and without original AMH levels). Results A total of 47 women were included in the analysis (42 assessed over two cycles; five one cycle only). CV of unexplained biological variability was 9.61%; analytical variability was 3.46%. Inter-participant variability, given by time-series plots of AMH levels, was greater than inter-cycle variability. Between individual participants, both mesor and peak-to-peak amplitudes proved variable. In addition, for each participant, intra-cycle variability was higher than inter-cycle variability. Conclusions Inter-participant and intra-cycle variability of AMH levels were greater than inter-cycle variability. Unexplained biological variability was higher than analytical variability using the Elecsys AMH Plus immunoassay. Understanding variability in AMH levels may aid in understanding differences in availability of antral ovarian follicles during the menstrual cycle, which may be beneficial in designing gonadotropin dosage for assisted reproductive technology.


2021 ◽  
Vol 156 (Supplement_1) ◽  
pp. S4-S4
Author(s):  
Erica Fatica ◽  
Sarah Jenkins ◽  
Renee Scott ◽  
Darci R Block ◽  
Jeffrey Meeusen ◽  
...  

Abstract The guideline-recommended lipid panel for cardiovascular disease (CVD) risk assessment measures total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and calculated low-density lipoprotein (LDL) cholesterol. Measured cholesterol in subfractions of HDL and LDL purportedly improve CVD risk prediction. Homogenous enzymatic methods are now available for measurement of the cholesterol within small dense LDL (sLDL), small dense HDL (HDL3), and triglyceride-rich lipoproteins (TRL). For meaningful interpretation of these measurements, an understanding of the potential sources and extent of result variability is needed. The smallest difference between serial measurements within a patient that likely reflects a change in clinical status is called the reference change value (RCV). Biological variability and reference change values (RCV) are well-characterized for basic lipids but there is limited information for sLDL, HDL3 or TRL. The objective of this study was to determine intra- and inter-individual variability for sLDL, HDL3, and TRL in a healthy reference population. Serum samples were collected from 24 healthy subjects (n=14 female/10 male) daily for three days (non-fasting), daily for five days (fasting), weekly for four weeks (fasting), and monthly for 7 months (fasting). sLDL, HDL3, and TRL cholesterol were measured in duplicate by enzymatic colorimetric assays (Denka, Japan) on a Roche Cobas c501. Each source of variability (between subject, within subject, and analytical) was calculated using random-effects regression models to estimate each variance component including the overall variation, standard deviation (SD), coefficient of variation (CV), and proportion of total variance (between-subject, within-subject, and analytical). Using these analytical and biological variances, the reference change value (RCV), index of individuality (IoI), and intraclass correlation coefficient (ICC) were determined. Analytic variability (CVa) from monthly testing was 1.2%, 1.1%, and 1.5% for sLDL, HDL3, and TRL, respectively. Monthly within-subject variability (CVw) was 17.1% for sLDL, 7.4% for HDL3 and 25.7% for TRL. Monthly between-subject variability (CVb) was 32.2%, 13.93%, and 33.4% for sLDL, HDL3, and TRL, respectively. Most of the monthly variation was attributed to between-subject variation for all three tests. Within-subject variation accounted for 37% of TRL variation and 22% for both sLDL and HLD3. Within-subject RCVs for monthly measurements were 16.9mg/dL for sLDL, 5.3mg/dL for HDL3, and 15.1mg/dL for TRL. IoIs for monthly testing were 0.81 for TRL, 0.57 for sLDL, and 0.61 for HDL3. Our data demonstrate that sLDL, HDL3, and TRL show low analytical variability, moderate within-subject variability, but high between-subject variability when measured by homogenous assays in a healthy population. The IoI value (>0.6) for TRL suggests use of a reference interval is appropriate for result interpretation. Conversely, clinical cut-points may be more useful than reference intervals for sLDL and HDL3 which had IoIs ~0.6. These findings may be useful for clinical interpretation, particularly when comparing successive measurements of these analytes.


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