Duration of Family Caregiving and Its Effects on Inflammation in the Caregiving Transitions Study

Sustained caregiving for older adult family members with disabilities can be a chronically stressful experience that may adversely affect the health of caregivers. Systemic inflammation is thought to be one mechanism by which caregiving stress might impact health, but previous studies of inflammation in caregivers have generally found inconsistent or very small effects with questionable clinical significance when comparing caregiving and non-caregiving control samples. The Caregiving Transitions Study (CTS) enrolled 283 family caregivers and 283 carefully-matched controls from an ongoing national epidemiologic study. This population-based sample of caregivers included an unusual subsample of 32 long-term caregivers who had been providing care to the same care recipients for over 9 consecutive years. Analyses of covariance indicated that these 32 long-term caregivers had statistically significant (p < 0.05) elevations on three circulating biomarkers of inflammation – C-reactive protein, Interleukin-6, and D-dimer – compared 1) to their 32 individually-matched non-caregiving controls, and 2) to the 248 caregivers who had been providing care for less than 9 years. Covariates in the analytic models included age, sex, race, and body mass index. Similar effects were observed for caregivers of persons with or without dementia. Polynomial regression models across all caregivers revealed significant curvilinear associations of inflammation with caregiving duration. Inflammation was not markedly elevated throughout the first several years of caregiving but then begin to increase more dramatically at around 10 years of caregiving. These findings suggest that long-term caregiving, in particular, may be associated with specific physical health risks through chronically elevated systemic inflammation.

Fuzzy Finite Element Analysis for Static Responses of Plane Structures

In this paper, a fuzzy finite element algorithm is investigated to determine static responses of plane structures. This algorithm concerns finite element method, fuzzy sets theory, and response surface method. Firstly, the notion of a standardized triangular fuzzy number is developed and utilized to replace original fuzzy numbers in the surrogate models. Then, the error estimations between the training and the test sets are performed to select the suitable response surface model amongst the regression models. Lastly, a good performance combination of complete and non-complete quadratic polynomial regression models is proposed to define the responses of structures. The merits of the proposed algorithm are illustrated via numerical examples.

Influence of Leaf Area Index on the Heat Index of a Tropic Urban Park

In the wake of climate change, cities need to adapt to global warming. In this context, the use of afforestation to improve the microclimate may assist in raising the quality of life for population. This objective requires research that analyzes how the variations in parameters related to canopy dynamics, such as the leaf area index (LAI) and photosynthetically active radiation (PAR) can influence thermal comfort indices. To contribute to this research, this study measured the air temperature, relative air humidity, PAR, and LAI on a monthly basis from July, 2017, to June, 2018, in an urban park in a tropical region of Brazil. Kriging maps were created for the heat index (HI), and multiple polynomial regression models were adjusted to estimate the HI using PAR and LAI data. After defining the models, positive and negative variations of LAI were tested to observe if any changes in HI occurred. The simulated results showed greater sensitivity to negative variations in LAI, in which a 50% reduction in LAI decreased the HI by 28%, particularly during the dry period.

A Clinical Study of AWT Measurement and Polynomial Regression Model Analysis in Critically Ill patients: A Retrospective Study

Background：In this study, a new measurement device was used to measure the AWT in critically ill patients and a polynomial regression model was applied to analyze the correlation between intra-abdominal hypertension (IAH) and AWT in critically ill patients.Methods: A retrospective analysis was conducted in critically ill patients who were admitted to the Department of Critical Care Medicine of Daping Hospital of Army Medical University from March 13, 2019, to May 23, 2020. According to the intravesical pressure (IVP) on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.Results: The AWT on the 1st and mean 7th day of the IAH group was (2.89±0.32)N/mm and (2.82±0.46) N/mm, respectively, which was higher than that of the non-IAH group [ (2.45±0.29) N/mm, (2.43±0.39) N/mm], p＜0.001. The average IVP on the 1st and mean 7th day of all patients were 12.78 (6.14, 18.99) and 11.49 (6.66, 19.43) mmHg, and the AWT on the 1st and mean 7th days were (2.75±0.38) and (2.75±0.47) N/mm, respectively, with significant differences (p< 0.0001). The polynomial regression models showed that the average AWT and IVP on the 1st and mean 7th were AWTday1=-2.450×10-3, IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean=-2.293×10-3, IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 2.73-2.97N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).Conclusion: There is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.Trial registration：ChiCTR，ChiCTR1900020562. Registered 8 January 2019，http://www.chictr.org.cn/showproj.aspx?proj=34441

A Clinical Study of AWT Measurement and Polynomial Regression Model Analysis in Critically Ill Patients

Objective: In this study, a new measurement device was used to measure the AWT in critically ill patients and a polynomial regression model was applied to analyze the correlation between intra-abdominal hypertension (IAH) and AWT in critically ill patients.Methods: A retrospective analysis was conducted in critically ill patients who were admitted to the Department of Critical Care Medicine of Daping Hospital of Army Medical University from August 30, 2018, to June 30, 2020. According to the intravesical pressure (IVP) on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.Results: The AWT on the 1st and mean 7th day of the IAH group was (2.89±0.32)N/mm and (2.82±0.46) N/mm, respectively, which was higher than that of the non-IAH group [ (2.45±0.29) N/mm, (2.43±0.39) N/mm], p＜0.001. The average IVP on the 1st and mean 7th day of all patients were 12.78 (6.14, 18.99) and 11.49 (6.66, 19.43) mmHg, and the AWT on the 1st and mean 7th days were (2.75±0.38) and (2.75±0.47) N/mm, respectively, with significant differences (p< 0.0001). The polynomial regression models showed that the average AWT and IVP on the 1st and mean 7th were AWTday1=-2.450×10-3, IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean=-2.293×10-3, IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 2.73-2.97N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).Conclusion: There is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.

Process optimization for the supercritical carbondioxide extraction of lycopene from ripe grapefruit (Citrus paradisi) endocarp

In this study, an underutilized citrus family fruit named grapefruit was explored for the extraction of lycopene using supercritical carbon dioxide (CO2) extraction technique. An experimental design was developed using response surface methodology to investigate the effect of supercritical carbon dioxide (CO2) operating parameter viz., pressure, temperature, CO2 flow rate, and extraction time on the extraction yield of lycopene yield from grapefruit. A total of 30 sets of experiments were conducted with six central points. The statistical model indicated that extraction pressure and extraction time individually, and their interaction, significantly affected the lycopene yield. The central composite design showed that the polynomial regression models developed were in agreement with the experimental results, with R2 of 0.9885. The optimum conditions for extraction of lycopene from grapefruit were 305 bar pressure, 35 g/min CO2 flow rate, 135 min of extraction time, and 70 °C temperature.

A Novel Approach to Improve Newborn Screening for Congenital Hypothyroidism by Integrating Covariate-Adjusted Results of Different Tests into CLIR Customized Interpretive Tools

Newborn screening for congenital hypothyroidism remains challenging decades after broad implementation worldwide. Testing protocols are not uniform in terms of targets (TSH and/or T4) and protocols (parallel vs. sequential testing; one or two specimen collection times), and specificity (with or without collection of a second specimen) is overall poor. The purpose of this retrospective study is to investigate the potential impact of multivariate pattern recognition software (CLIR) to improve the post-analytical interpretation of screening results. Seven programs contributed reference data (N = 1,970,536) and two sets of true (TP, N = 1369 combined) and false (FP, N = 15,201) positive cases for validation and verification purposes, respectively. Data were adjusted for age at collection, birth weight, and location using polynomial regression models of the fifth degree to create three-dimensional regression surfaces. Customized Single Condition Tools and Dual Scatter Plots were created using CLIR to optimize the differential diagnosis between TP and FP cases in the validation set. Verification testing correctly identified 446/454 (98%) of the TP cases, and could have prevented 1931/5447 (35%) of the FP cases, with variable impact among locations (range 4% to 50%). CLIR tools either as made here or preferably standardized to the recommended uniform screening panel could improve performance of newborn screening for congenital hypothyroidism.

Trend of preventable neonatal mortality in the States of Brazil

Objectives: to describe the trend of preventable neonatal mortality due to interventions by the Unified Health System in Brazil from 2000 to 2018, according to groups of causes of death and maternal residence. Methods: mixed ecological study with data from the Mortality Information System and Information System on Live Births. The analysis occurred based on the number and rates of avoidable neonatal mortality, polynomial regression models by least squares method and thematic maps. Results: the avoidable neonatal mortality rate decreased from 10.98 in 2000 to 6.76 per 1,000 live births in 2018. Preventable causes prevailed due to adequate care for women during pregnancy, childbirth, fetus and newborn. Deaths from preventable causes from health promotion actions during pregnancy increased in Maranhão (p=0.003) and the Federal District (p=0.001) and remained stable in nine states. There was stability in the rates of mortality due to delivery in Maranhão, Piauí and Amazonas. The causes avoidable by actions with the newborn showed a decreasing trend, except for Roraima where there was stability. Conclusions: there are inequalities in trends of avoidable neonatal mortality rates in the states second according to the group of causes and the need to improve access to and quality of maternal and child health care in these places

Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.