Information and scientific impact of Advanced Therapies in the age of mass media: an Altmetrics-based analysis of Tissue Engineering. (Preprint)

2020 ◽  
Author(s):  
Antonio Santisteban ◽  
Julia Moran ◽  
Miguel Ángel Martín Piedra ◽  
Antonio Campos Muñoz ◽  
José Antonio Moral Muñoz ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Mass Media ◽  
Prediction Equation ◽  
Factorial Analysis ◽  
Online Media ◽  
Research Groups ◽  
Academic Institutions ◽  
Scientific Impact ◽  
Online Platforms ◽  
The Impact

BACKGROUND Tissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-staged organs. Its development has taken placed since the last decade of the 20th century, entailing a clinical revolution. In this sense, TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact. OBJECTIVE To evaluate TE online dimension from 2012 to 2018 by using metadata obtained from the Web of Science (WoS) and Altmetrics and to develop a prediction equation for the impact of TE documents from Almetrics scores. METHODS We have analyzed 23,719 TE documents through descriptive and statistical methods. First, TE temporal evolution was exposed for WoS and fifteen online platforms (News, Blogs, Policy, Twitter, Patents, Peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q&A, Video and Mendeley readers). The 10 most-cited TE original articles were ranked according to WoS citations and the Altmetric Attention Score. Second, in order to better comprehend TE online framework, a correlation and factorial analysis were performed based on the suitable results previously obtained for the Bartlett Sphericity and Kaiser-Meyer-Olkin tests. Finally, the liner regression model was applied to elucidate the relation between academy and online media and to construct a prediction equation for TE from Altmetrics data. RESULTS TE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers and Altmetric Scores. However, WoS and Altmetrics rankings for the most cited documents clearly differs. When compared, the best correlation results were obtained for Mendeley readers and WoS (ρ=0.71). In addition, the factorial analysis identified six factors that could explain the previously observed differences between TE academy, and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than the 40% of TE WoS citations from Altmetrics scores. CONCLUSIONS The scientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because of the focus of TE research importantly differs when the academic institutions and online platforms are compared, it could be stated that basic and clinical research groups, academic institutions and health politicians should take it into account in a coordinated effort oriented to the design and implementation of adequate strategies for information diffusion and population health education.

scholarly journals Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 853
Author(s):  
Jee-Yun Kim ◽  
Jeong Yee ◽  
Tae-Im Park ◽  
So-Youn Shin ◽  
Man-Ho Ha ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Linear Regression ◽  
Parenteral Nutrition ◽  
Prediction Model ◽  
Scoring System ◽  
Prediction Equation ◽  
Icu Patients ◽  
Risk Scoring ◽  
Risk Scoring System

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.

scholarly journals A Study of Developing a Prediction Equation of Electricity Energy Output via Photovoltaic Modules

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1503
Author(s):  
Minsu Kim ◽  
Hongmyeong Kim ◽  
Jae Hak Jung
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Air Temperature ◽  
Temperature Difference ◽  
Prediction Accuracy ◽  
Prediction Equation ◽  
Energy Output ◽  
Photovoltaic Modules ◽  
Mean Error ◽  
Pv Module

Various equations are being developed and applied to predict photovoltaic (PV) module generation. Currently, quite diverse methods for predicting module generation are available, with most equations showing accuracy with ≤5% error. However, the accuracy can be determined only when the module temperature and the value of irradiation that reaches the module surface are precisely known. The prediction accuracy of outdoor generation is actually extremely low, as the method for predicting outdoor module temperature has extremely low accuracy. The change in module temperature cannot be predicted accurately because of the real-time change of irradiation and air temperature outdoors. Calculations using conventional equations from other studies show a mean error of temperature difference of 4.23 °C. In this study, an equation was developed and verified that can predict the precise module temperature up to 1.64 °C, based on the experimental data obtained after installing an actual outdoor module.

Predicting in Vivo Soft Tissue Masses of the Lower Extremity Using Segment Anthropometric Measures and DXA

2005 ◽  
Vol 21 (4) ◽  
pp. 371-382 ◽  
Author(s):  
Jeffrey D. Holmes ◽  
David M. Andrews ◽  
Jennifer L. Durkin ◽  
James J. Dowling
Keyword(s):  
Soft Tissue ◽  
Lower Extremity ◽  
Prediction Equation ◽  
Regression Equations ◽  
Anthropometric Measures ◽  
Soft Tissue Masses ◽  
Highly Correlated ◽  
Relative Errors ◽  
Dxa Scans

The purpose of this study was to derive and validate regression equations for the prediction of fat mass (FM), lean mass (LM), wobbling mass (WM), and bone mineral content (BMC) of the thigh, leg, and leg + foot segments of living people from easily measured segmental anthropometric measures. The segment masses of 68 university-age participants (26 M, 42 F) were obtained from full-body dual photon x-ray absorptiometry (DXA) scans, and were used as the criterion values against which predicted masses were compared. Comprehensive anthropometric measures (6 lengths, 6 circumferences, 8 breadths, 4 skinfolds) were taken bilaterally for the thigh and leg for each person. Stepwise multiple linear regression was used to derive a prediction equation for each mass type and segment. Prediction equations exhibited high adjustedR2values in general (0.673 to 0.925), with higher correlations evident for the LM and WM equations than for FM and BMC. Predicted (equations) and measured (DXA) segment LM and WM were also found to be highly correlated (R2= 0.85 to 0.96), and FM and BMC to a lesser extent (R2= 0.49 to 0.78). Relative errors between predicted and measured masses ranged between 0.7% and –11.3% for all those in the validation sample (n= 16). These results on university-age men and women are encouraging and suggest that in vivo estimates of the soft tissue masses of the lower extremity can be made fairly accurately from simple segmental anthropometric measures.

scholarly journals Predicting Daily Dry Matter Intake Using Feed Intake of First Two Hours after Feeding in Mid and Late Lactation Dairy Cows with Fed Ration Three Times Per Day

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 104
Author(s):  
Shulin Liang ◽  
Chaoqun Wu ◽  
Wenchao Peng ◽  
Jian-Xin Liu ◽  
Hui-Zeng Sun
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Dry Matter ◽  
Prediction Equation ◽  
Dry Matter Intake ◽  
Validation Dataset ◽  
Nutrient Requirements ◽  
Independent Dataset ◽  
Lactating Dairy Cows ◽  
Proposed Model

The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R2 = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R2 (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day.

scholarly journals Ethnic Specific body fat percent prediction equation as surrogate marker of obesity in Ethiopian adults

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Makeda Sinaga ◽  
Melese Sinaga Teshome ◽  
Tilhun Yemane ◽  
Elsah Tegene ◽  
David Lindtsrom ◽  
...  
Keyword(s):  
Body Fat ◽  
Age Groups ◽  
Surrogate Marker ◽  
Cost Effective ◽  
Prediction Equation ◽  
Prediction Equations ◽  
Altman Plot ◽  
Cross Sectional ◽  
Bland Altman Plot ◽  
Body Fat Percent

Abstract Background Application of advanced body composition measurement methods is not practical in developing countries context due to cost and unavailability of facilities. This study generated ethnic specific body fat percent prediction equation for Ethiopian adults using appropriate data. Methods A cross-sectional study was carried ifrom February to April 2015 among 704 randomly selected adult employees of Jimma University. Ethnic specific Ethiopian body fat percent (BF%) prediction equation was developed using a multivariable linear regression model with measured BF% as dependent variable and age, sex, and body mass index as predictor variables. Agreement between fat percent measured using air displacement plethysmography and body fat percent estimated using Caucasian prediction equations was determined using Bland Altman plot. Results Comparison of ADP measured and predicted BF% showed that Caucasian prediction equation underestimated body fat percent among Ethiopian adults by 6.78% (P < 0.0001). This finding is consistent across all age groups and ethnicities in both sexes. Bland Altman plot did not show agreement between ADP and Caucasian prediction equation (mean difference = 6.7825) and some of the points are outside 95% confidence interval. The caucasian prediction equation significantly underestimates body fat percent in Ethiopian adults, which is consistent across all ethnic groups in the sample. The study developed Ethnic specific BF% prediction equations for Ethiopian adults. Conclusion The Caucasian prediction equation significantly underestimates body fat percent among Ethiopian adults regardless of ethnicity. Ethiopian ethnic-specific prediction equation can be used as a very simple, cheap, and cost-effective alternative for estimating body fat percent among Ethiopian adults for health care provision in the prevention of obesity and related morbidities and for research purposes.

scholarly journals Attenuation relationships of peak ground motions in the Jazan Region

2021 ◽  
Vol 14 (3) ◽  
Author(s):  
Ali K. Abdelfattah ◽  
Abdullah Al-amri ◽  
Kamal Abdelrahman ◽  
Muhamed Fnais ◽  
Saleh Qaysi
Keyword(s):  
Saudi Arabia ◽  
Ground Motions ◽  
Prediction Equation ◽  
Peak Ground Velocity ◽  
Local Magnitude ◽  
Ground Acceleration ◽  
Data Set ◽  
Hypocentral Distance ◽  
Distance Range ◽  
Attenuation Relationships

AbstractIn this study, attenuation relationships are proposed to more accurately predict ground motions in the southernmost part of the Arabian Shield in the Jazan Region of Saudi Arabia. A data set composed of 72 earthquakes, with normal to strike-slip focal mechanisms over a local magnitude range of 2.0–5.1 and a distance range of 5–200 km, was used to investigate the predictive attenuation relationship of the peak ground motion as a function of the hypocentral distance and local magnitude. To obtain the space parameters of the empirical relationships, non-linear regression was performed over a hypocentral distance range of 4–200 km. The means of 638 peak ground acceleration (PGA) and peak ground velocity (PGV) values calculated from the records of the horizontal components were used to derive the predictive relationships of the earthquake ground motions. The relationships accounted for the site-correlation coefficient but not for the earthquake source implications. The derived predictive attenuation relationships for PGV and PGA are$$ {\log}_{10}(PGV)=-1.05+0.65\cdotp {M}_L-0.66\cdotp {\log}_{10}(r)-0.04\cdotp r, $$ log 10 PGV = − 1.05 + 0.65 · M L − 0.66 · log 10 r − 0.04 · r , $$ {\log}_{10}(PGA)=-1.36+0.85\cdotp {M}_L-0.85\cdotp {\log}_{10}(r)-0.005\cdotp r, $$ log 10 PGA = − 1.36 + 0.85 · M L − 0.85 · log 10 r − 0.005 · r , respectively. These new relationships were compared to the grand-motion prediction equation published for western Saudi Arabia and indicate good agreement with the only data set of observed ground motions available for an ML 4.9 earthquake that occurred in 2014 in southwestern Saudi Arabia, implying that the developed relationship can be used to generate earthquake shaking maps within a few minutes of the event based on prior information on magnitudes and hypocentral distances taking into considerations the local site characteristics.

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