Mixed Infant Feeding Is Not Associated With Increased Risk of Decelerated Growth Among WIC-Participating Children in Southern California

Background: The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) provides nutrition assistance to half of infants born in the United States. The nationally representative WIC Infants and Toddler Feeding Practices Study-2 (ITFPS-2) reported a caloric deficit at 7 months among infants receiving WIC mixed feeding packages, suggesting these infants may be at risk for growth deceleration/faltering.Methods: Longitudinal administrative data collected prospectively from WIC participants in Southern California between 2010 and 2019 were used (n = 16,255). Infant lengths and weights were used to calculate weight-for-length (WLZ), weight-for-age (WAZ) and length-for-age (LAZ) z-scores at different time points. Growth deceleration/faltering was determined at 9, 12, 18, and 24 months by the change in z-score from the last measurement taken ≤ 6 months of age. Infant feeding was categorized by the food package (breastfeeding, mixed feeding, and formula feeding) infants received from WIC at 7 months. Poisson regression models were used to evaluate the association between WIC infant package at 7 months and deceleration/faltering at 9, 12, 18, and 24 months.Results: The proportion of infants displaying decelerated/faltering growth was low for all infant food package groups. Receiving the WIC mixed feeding package at 7 months of age was not associated with WLZ, WAZ, and LAZ deceleration/faltering growth.Conclusions: Growth deceleration/faltering rates were very low among WIC participating children in Southern California, highlighting the critical role of nutrition assistance in supporting adequate growth in early childhood.

Do Sanctions Cause Economic Growth Collapses?

This chapter explores whether economic sanctions are able to trigger sudden economic growth collapses. The primarily aim of economic sanctions is to cause a political or behavioural change by imposing serious restrictions on important economic activities undertaken by the target country. In particular, the basic idea is that sanctions cause a large adverse and sudden shock to the target’s economy. It assumes that when this shock is severe enough, the target country is more willing to cooperate. The findings reported in this chapter clearly demonstrate that economic sanctions have a significant positive effect on the likelihood of a growth deceleration in the first three years after the first threat signals or actual imposition. It turns out that not all sanctions are equally successful in creating a sudden economic shock. In particular, trade sanctions, multilateral sanctions, and sanctions aimed at the business sector are the most harmful for the economy of the target country.

GnRHa/Stanozolol Combined Therapy Maintains Normal Bone Growth in Central Precocious Puberty

BackgroundGonadotropin-releasing hormone agonist (GnRHa) is the gold standard in the treatment of Central Precocious Puberty (CPP) with progressive puberty and accelerative growth. However, GnRHa treatment is reported to result in growth deceleration and prevents growth plate development which leads to a reduction in height velocity. Stanozolol (ST) has been used to stimulate growth in patients with delayed growth and puberty, nevertheless, the effects and mechanisms of ST on CPP with GnRHa treatment are currently unclear.Methods and ResultsIn the current study, we recorded the following vital observations that provided insights into ST induced chondrogenic differentiation and the maintenance of normal growth plate development: (1) ST efficiently prevented growth deceleration and maintained normal growth plate development in rats undergoing GnRHa treatment; (2) ST suppressed the inhibitory effect of GnRHa to promote chondrogenic differentiation; (3) ST induced chondrogenic differentiation through the activation of the JNK/c-Jun/Sox9 signaling pathway; (4) ST promoted chondrogenic differentiation and growth plate development through the JNK/Sox9 signaling pathway in vivo.ConclusionsST mitigated the inhibitory effects of GnRHa and promoted growth plate development in rats. ST induced the differentiation of chondrocytes and maintained normal growth plate development through the activation of JNK/c-Jun/Sox9 signaling. These novel findings indicated that ST could be a potential agent for maintaining normal bone growth in cases of CPP undergoing GnRHa treatment.

Does Fetal Growth Adequacy Affect the Nutritional Composition of Mothers' Milk?: A Historical Cohort Study

Objective The study aimed to assess the association between intrauterine growth of preterm infants and energy and macronutrient contents in their mothers' milk. Study Design A historical cohort of mothers of preterm infants was assessed according to offspring's intrauterine growth. Fetal growth restriction (FGR) was defined as small-for-gestational age or appropriate for gestational age with fetal growth deceleration. During the first 4 weeks after delivery, the composition of daily pool samples of mothers' milk was measured by using a mid-infrared human milk analyzer. Explanatory models for milk energy, true protein, total carbohydrate, and fat contents were obtained by generalized additive mixed effects regression models. Results In total, 127 milk samples were analyzed from 73 mothers who delivered 92 neonates. Energy content was significantly higher in mothers with chronic hypertension (average: +6.28 kcal/dL; 95% confidence interval [CI]: 0.54–12.01; p = 0.034) and for extremely preterm compared with very preterm infants (average: +5.95 kcal/dL; 95% CI: 2.16–9.73; p = 0.003), and weakly associated with single pregnancies (average: +3.38 kcal/dL; 95% CI: 0.07–6.83; p = 0.057). True protein content was significantly higher in mothers with chronic hypertension (average: +0.91 g/dL; 95% CI: 0.63–1.19; p < 0.001) and with hypertension induced by pregnancy (average: +0.25 g/dL, 95% CI: 0.07–0.44; p = 0.007), and for extremely preterm compared with very and moderate preterm infants (average: +0.19; 95% CI: 0.01–0.38; p = 0.043 and +0.28 g/dL; 95% CI: 0.05–0.51; p = 0.017, respectively). Fat content was weakly and negatively associated with FGR, both in SGA infants and AGA infants with fetal growth deceleration (average: −0.44 g/dL; 95% CI: −0.92 to −0.05; p = 0.079 and average: −0.36 g/dL; 95% CI: −0.74 to −0.02; p = 0.066, respectively). Conclusion Energy and macronutrient contents in mothers' milk of preterm infants was significantly and positively associated with the degree of prematurity and hypertension. The hypothesis that the composition of milk is associated with FGR was not demonstrated. Key Points

Novel Uses of Three-Parameter Logistic Models and First-Derivative Models for the Coronavirus Disease (COVID-19) Epidemic in the United States, in Three Distinct Scenarios

Background:Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020.Methods:Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases.Results:All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11.Conclusion:Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

The digital great leap forward mapping China's 21st century attempt to create a new growth model

The recent successes of the Chinese modernisation strategy are substantiated by an array of indicators showing an impressive improvement. Irrespective of China's current growth deceleration, these indicators suggest a highly effective implementation of an ambitious roadmap that can ultimately help China to catch up and achieve a global technological leadership. Still, some scholars point to deep structural deficiencies, and maintain that these indicators – however impressive they are – merely scratch the surface, while much deeper change is required in order to maintain economic growth. Therefore, the purpose of this paper (finalized before the ongoing COVID-19 crisis) is to contribute to this burgeoning literature – documenting the outcome and analysing the implications of China's efforts to embrace a new growth model – and analyse the chances of the Chinese digital great leap forward, that is the radical transformation of its prior modernisation trajectory. Drawing on a systematic review of the literature, the author maps, presents and analyses existing indicators quantifying China's progress in shifting to this new development trajectory, identifying also the gaps in the conventional measurement approaches. According to the findings of this paper, there are several easy-to-measure indicators, often used in international comparisons, that indeed confirm the optimistic scenario of China's development prospects in the near future. On the other hand, some hard-to-quantify factors, such as the localization of knowledge and the spreading of innovation, need to be also considered. These latter show a closer association with countries' development level as well as development potential. With regards to these latter particularities, China still has a long way to go.

Mismatch between poor fetal growth and rapid postnatal weight gain in the first 2 years of life is associated with higher blood pressure and insulin resistance without increased adiposity in childhood: the GUSTO cohort study

Background Using longitudinal ultrasounds as an improved fetal growth marker, we aimed to investigate if fetal growth deceleration followed by rapid postnatal weight gain is associated with childhood cardiometabolic risk biomarkers in a contemporary well-nourished population. Methods We defined fetal growth deceleration (FGD) as ultrasound-measured 2nd-3rd-trimester abdominal circumference decrease by ≥0.67 standard deviation score (SDS) and rapid postnatal weight gain (RPWG) as 0–2-year-old weight increase by ≥0.67 SDS. In the GUSTO mother-offspring cohort, we grouped 797 children into four groups of FGD-only (14.2%), RPWG-only (23.3%), both (mismatch, 10.7%) or neither (reference, 51.8%). Adjusting for confounders and comparing with the reference group, we tested associations of these growth groups with childhood cardiometabolic biomarkers: magnetic resonance imaging (MRI)-measured abdominal fat (n = 262), liver fat (n = 216), intramyocellular lipids (n = 227), quantitative magnetic resonance-measured overall body fat % (BF%) (n = 310), homeostasis model assessment of insulin resistance (HOMA-IR) (n = 323), arterial wall thickness (n = 422) and stiffness (n = 443), and blood pressure trajectories (ages 3–6 years). Results Mean±SD birthweights were: FGD-only (3.11 ± 0.38 kg), RPWG-only (3.03 ± 0.37 kg), mismatch (2.87 ± 0.31 kg), reference (3.30 ± 0.36 kg). FGD-only children had elevated blood pressure trajectories without correspondingly increased BF%. RPWG-only children had altered body fat partitioning, higher BF% [BF = 4.26%, 95% confidence interval (CI) (2.34, 6.19)], HOMA-IR 0.28 units (0.11, 0.45)] and elevated blood pressure trajectories. Mismatch children did not have increased adiposity, but had elevated ectopic fat, elevated HOMA-IR [0.29 units (0.04,0.55)] and the highest blood pressure trajectories. Associations remained even after excluding small-for-gestational-age infants from analyses. Conclusions Fetal growth deceleration coupled with rapid postnatal weight gain was associated with elevated childhood cardiometabolic risk biomarkers without correspondingly increased BF%.

Novel uses of three-parameter logistic models and first-derivative models for the Coronavirus Disease (COVID-19) epidemic in the United States, in three distinct scenarios

Background: Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020.Methods: Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases.Results: All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11.Conclusion: Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

Novel uses of three-parameter logistic models and first-derivative models for the Coronavirus Disease (COVID-19) epidemic in the United States, in three distinct scenarios

Background Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020. Methods Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases. Results All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11. Conclusion Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.