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Sources of data/modeling
Published in Edward M. Rafalski, Ross M. Mullner, Healthcare Analytics, 2022
Edward M. Rafalski, Robert Marksthaler
Various mathematical modeling techniques were used in addition to the SIR model. The exponential model has been widely successful in capturing the increase in COVID-19 cases during the phases that developed most rapidly and thus were the most difficult to mitigate. Based on a similar principle as doubling time, the exponential model takes a simple form and essentially captures the effect of repeated doubling over time (1, 2, 4, 8, …). In this modeling, predicted values of the exponential were determined by linear regression conducted on log (numbers of cases). Another was the Polynomial (2nd-order) model. A 2nd-order polynomial (y ~ x2 + x) captures quadratic growth and is the expected outcome when the growth rate changes and when that rate of change is constant. The rate of increase in this model is initially faster than that of the exponential model. From this model were obtained predicted values using numerical optimization of parameters and curve fitting. Logistic modeling was also used. When exponential growth slows and tapers-off, the growth curve often becomes logistic, that is, “S” shaped. The rate of increase in this model is initially exponential but slows as an upper limit is approached. Predicted values could be obtained from this model using numerical optimization of parameters and curve fitting.
Methods of nutritional assessment and surveillance
Published in Geoffrey P. Webb, Nutrition, 2019
Body size is not only dependent upon environmental factors, like diet, but is also genetically determined. Some races of people may be genetically smaller than others and this is another factor that makes the use of non-local standards problematical. Within races, there is a natural biological variation in the genetic component of height. This will obviously create difficulties in assessing the nutritional status of an individual child from a single set of anthropometric measurements. Is a child short because it is genetically small or because it is chronically malnourished? Growth curves are more useful ways of monitoring the nutritional status of individual children. Provided the child remains on its predicted growth curve, then whether the absolute values are low or high is in most cases unimportant. If a child on a high growth curve dips significantly below that curve, this may indicate a nutrition or health problem even though it may be close to average for its age. A child who remains on a low growth curve will be assessed as growing satisfactorily even though it may be below the average size for its age.
Bioprocess Parameters of Production of Cyanobacterial Exopolysaccharide
Published in Gokare A. Ravishankar, Ranga Rao Ambati, Handbook of Algal Technologies and Phytochemicals, 2019
Onkar Nath Tiwari, Sagnik Chakraborty, Indrama Devi, Abhijit Mondal, Biswanath Bhunia, Shen Boxiong
Cyanobacteria is often cultured as batch, semi-continuous, or in continuous regime. Each of these modes have advantages and constraints. The simplest is the batch mode, where resources are finite, and cell concentration continually increases until some factor becomes limiting (typically some nutrient is exhausted). Potential products also increase their intensity in the medium over time. For growth restoration, the limiting factors need to be replenished. The batch growth is a highly dynamic process with culture density increasing as the typical sigmoid growth curve. Individual phases of batch growth can be categorized into lag, acceleration, exponential, retardation (“linear”), stationary, and decline period (Finkel 2006).
From growth charts to growth status: how concepts of optimal growth and tempo influence the interpretation of growth measurements
Published in Annals of Human Biology, 2023
The DSGS also published BMI charts for children 2–20 years of age (Zemel et al. 2015). In part, the rationale for DS-specific BMI charts was based on the known differences in body proportions in people with DS, specifically shorter limb lengths, compared to the general population. This may affect BMI distributions. Nevertheless, the publication of DS-specific BMI charts evoked controversy because of the high rate of obesity among children with DS. This concern mirrors concerns in the general population regarding the use of contemporary data to create BMI charts for the general population (as discussed above). As noted in the news release regarding the new DS growth charts by the American Academy of Paediatrics, “The growth curves … represent current trends but not necessarily optimal growth.” Dr. Marilyn Bull, author of the Academy’s 2011 clinical report on Down syndrome, said “… children with Down syndrome tend to have low metabolic rates, and some have poor diets. Until optimal BMI guidelines for individuals with Down syndrome are established, clinicians should use the BMI guidelines of the CDC charts” (Jenco 2015).
Is there clinical consensus in defining weight restoration for adolescents with anorexia nervosa?
Published in Eating Disorders, 2018
Jocelyn Lebow, Leslie A. Sim, Erin C. Accurso
An alternative method of determining EBW uses the adolescent’s BMI percentile history throughout development. This method relies on obtaining a growth history with multiple measurements and has the benefit of accounting for stunted height and individual differences in BMI. The reliability of this method, however, depends on the clarity and availability of an adolescent’s growth data prior to the onset of AN. Further, interpretation of growth curves depends on some subjectivity on the part of the clinician. As such, using growth curves may be problematic for intervention studies that rely on objective and consistent measures. An equally individualized approach utilizes psychological and behavioral indicators to assess when weight restoration is achieved. This latter approach, however, does not allow for prediction of EBW and may be less accurate when psychological and behavioral change lags behind weight restoration.
Diphenyl diselenide suppresses key virulence factors of Candida krusei, a neglected fungal pathogen
Published in Biofouling, 2022
Bruna Graziele Marques da Silva, Ana Paula Pinto, Juliene Cristina da Silva Passos, João Batista Teixeira da Rocha, Carlos Alberto-Silva, Maricilia Silva Costa
(PhSe)2 suppressed C. krusei growth, a clinically relevant NAC species, at a concentration-dependent manner in different cell densities (104, 105, and 106 cells mL−1), with doses larger than 10 μΜ inhibiting 100% of cell growth. (PhSe)2 also changed the growth time-curve profile C. krusei when concentrations > 5 μΜ were tested, reducing μmax values and correlation index (R2) in linear regression analyses compared to untreated fungi or treated with (p-Cl-PhSe)2. A microorganism growth curve is represented by well-defined phases (lag, exponential or log, stationary, and death or decline) (Finkel 2006). The lag phase is a period of time during which gene regulation is adapted to adjust to new growth conditions; it includes the activation of signaling pathways and transcriptional modifications that lead to protein assembly upregulation (Vermeersch et al. 2019). Growth kinetic parameters among clinically relevant C. albicans, C. glabrata, C. parapsilosis, C. tropicalis, and C. krusei showed differences in average growth rate and time required to start the exponential phase of growth (lag phase) (Bordallo-Cardona et al. 2019). (PhSe)2 significantly increased the time duration of the lag phase and delayed the start of the exponential phase in C. krusei growth kinetics in our study, and these results create new possibilities for investigating its effects on regulatory pathways in Candida species needed for differentiation and multiplication, which can facilitate exponential growth.