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Year : 2019
 Volume
: 22  Issue : 4  Page
: 407411 

Application of student's ttest, analysis of variance, and covariance 

Prabhaker Mishra^{1}, Uttam Singh^{1}, Chandra M Pandey^{1}, Priyadarshni Mishra^{2}, Gaurav Pandey^{3}
^{1} Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India ^{2} Department of Ophthalmology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India ^{3} Department of Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
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Date of Web Publication  4Oct2019 




Abstract   
Student's t test (t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, first gets a common P value. A significant P value of the ANOVA test indicates for at least one pair, between which the mean difference was statistically significant. To identify that significant pair(s), we use multiple comparisons. In ANOVA, when using one categorical independent variable, it is called oneway ANOVA, whereas for two categorical independent variables, it is called twoway ANOVA. When using at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA. When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.
Keywords: Student's t test, analysis of variance, analysis of covariance, oneway, twoway
How to cite this article: Mishra P, Singh U, Pandey CM, Mishra P, Pandey G. Application of student's ttest, analysis of variance, and covariance. Ann Card Anaesth 2019;22:40711 
How to cite this URL: Mishra P, Singh U, Pandey CM, Mishra P, Pandey G. Application of student's ttest, analysis of variance, and covariance. Ann Card Anaesth [serial online] 2019 [cited 2022 Nov 29];22:40711. Available from: https://www.annals.in/text.asp?2019/22/4/407/268565 
Introduction   
Student's t test (t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. For these methods, testing variable (dependent variable) should be in continuous scale and approximate normally distributed. Mean is the representative measure for normally distributed continuous variable and statistical methods used to compare between the means are called parametric methods. For nonnormal continuous variable, median is representative measure, and in this situation, comparison between the groups is performed using nonparametric methods. Most parametric test has an alternative nonparametric test.^{[1],[2],[3]}
There are many statistical tests within Student's t test (t test), ANOVA and ANCOVA, and each test has its own assumptions. Although not every method is popular, some of them can be managed from other available methods. The aim of the present article is to discuss the assumptions, application, and interpretation of the some popular T, ANOVA, and ANCOVA methods i.e., one sample t test, independent samples t test, paired samples t test, oneway ANOVA, twoways ANOVA, oneway repeated measures ANOVA, twoways repeated measures ANOVA, oneway ANCOVA, and Oneway repeated measures ANCOVA. To understand the above statistical methods, an example [Table 1] with a data set of 20 patients whose age groups, gender, body mass index (BMI), and diastolic blood pressure (DBP) measured at baseline (B/L), 30 min and 60 min are given below. Further, examples related to the above statistical methods are discussed from the given data.
T Test, ANOVA, and ANCOVA   
Basic concepts
The Student's t test (also called T test) is used to compare the means between two groups and there is no need of multiple comparisons as unique P value is observed, whereas ANOVA is used to compare the means among three or more groups.^{[4],[5]} In ANOVA, the first gets a common P value. A significant P value of ANOVA test indicates for at least one pair, between which the mean difference was statistically significant.^{[6]} To identify that significant pair(s), posthoc test (multiple comparisons) is used. In ANOVA test, when at least one covariate (continuous variable) is adjusted to remove the confounding effect from the result called ANCOVA. ANOVA test (F test) is called “Analysis of Variance” rather than “Analysis of Means” because inferences about means are made by analyzing variance.^{[7],[8],[9]}
Steps in hypothesis testing
Hypothesis building
Like other tests, there are two kinds of hypotheses; null hypothesis and alternative hypothesis. The alternative hypothesis assumes that there is a statistically significant difference exists between the means, whereas the null hypothesis assumes that there is no statistically significant difference exists between the means.
Computation of test statistics
In these test, first step is to calculate test statistics (called t value in student's t test and F value in ANOVA test) also called calculated value. It is calculated after putting inputs (from the samples) in statistical test formula. In student's t test, calculated t value is ratio of mean difference and standard error, whereas in the ANOVA test, calculated F value is ratio of the variability between groups with the variability of the observations within the groups.^{[1],[4]}
Tabulated value
At degree of freedom of the given observations and desired level of the confidence (usually at twosided test, which is more powerful than onesided test), corresponding tabulated value of the T test or F test is selected (from the statistical table).^{[1],[4]}
Comparison of calculated value with tabulated value and null hypothesis
If the calculated value is greater than the tabulated value, then reject the null hypothesis where null hypothesis states that means are statistically same between the groups.^{[1],[4]} As the sample size increases corresponding degree of freedom also increases. For a given level of confidence, higher degree of freedom has lower tabulated value. That's the reason, when the sample size increases, its significance level also improves (i.e., P value is decreasing).
T Test
It is one of the most popular statistical techniques used to test whether mean difference between two groups is statistically significant. Null hypothesis stated that both means are statistically equal, whereas alternative hypothesis stated that both means are not statistically equal i.e., they are statistically different to each other.^{[1],[3],[7]} T test are three types i.e., one sample t test, independent samples t test, and paired samples t test.
Onesample t test
The one sample t test is a statistical procedure used to determine whether mean value of a sample is statistically same or different with mean value of its parent population from which sample was drawn. To apply this test, mean, standard deviation (SD), size of the sample (Test variable), and population mean or hypothetical mean value (Test value) are used. Sample should be continuous variable and normally distributed.^{[1],[9],[10],[11]} Onesample t test is used when sample size is <30. In case sample size is ≥30 used to prefer one sample z test over one sample t test although for one sample z test, population SD must be known. If population SD is not known, one sample t test can be used at any sample size. In one sample Z test, tabulated value is z value (instead of t value in one sample t test). To apply this test through popular statistical software i.e., statistical package for social sciences (SPSS), option can be found in the following menu [Analyze – compare means – onesample t test].
Example: From [Table 1], BMI (mean ± SD) was given 24.45 ± 2.19, whereas population mean was assumed to be 25.5. One sample t test indicated that mean difference between sample mean and population mean was statistically significantly different to each other (P = 0.045).
Independent samples t test
The independent t test, also called unpaired t test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated (independent) groups?
To apply this test, a continuous normally distributed variable (Test variable) and a categorical variable with two categories (Grouping variable) are used. Further mean, SD, and number of observations of the group 1 and group 2 would be used to compute significance level. In this procedure, first significance level of Levene's test is computed and when it is insignificant (P > 0.05), equal variances otherwise (P < 0.05), unequal variances are assumed between the groups and according P value is selected for independent samples t test.^{[1],[10],[11],[12]} In SPSS [Analyze – compare means – independent samples t test].
Example: From [Table 1], mean BMI of the male (n = 10) and female (n = 10) were 24.80 ± 2.20 and 24.10 ± 2.23, respectively. Levene's test (p = 0.832) indicated that variances between the groups were statistically equal. At equal variances assumed, independent samples t test (p = 0.489) indicated that mean BMI of the male and female was statistically equal.
Paired samples t test
The paired samples t test, sometimes called the dependent samples ttest, is used to determine whether the change in means between two paired observations is statistically significant? In this test, same subjects are measured at two time points or observed by two different methods.^{[4]} To apply this test, paired variables (prepost observations of same subjects) are used where paired variables should be continuous and normally distributed. Further mean and SD of the paired differences and sample size (i.e., no. of pairs) would be used to calculate significance level.^{[1],[11],[13]} In SPSS [Analyze – compare means – paired samples t test].
Example: From [Table 1], DBP of the 20 patients (mean ± SD); at baseline, 30 min and paired differences (difference between baselines and 30 min) were 79.55 ± 4.87, 83.90 ± 5.58, and 4.35 ± 4.16. Paired samples t test indicated that mean difference of paired observations of DBP between baseline and 30 min was statistically significant (P < 0.001).
ANOVA test (F test)
A statistical technique used to compare the means between three or more groups is known as ANOVA or F test. It is important that ANOVA is an omnibus test statistic. Its significant P value indicates that there is at least one pair in which the mean difference is statistically significant. To determine the specific pair's, post hoc tests (multiple comparisons) are used. There are various ANOVAs test, and their objectives are varying from one test to another. There are two main types of ANOVA i.e., oneway ANOVA and oneway repeated measures ANOVA. First is used for independent observations and later for dependent observations. When used one categorical independent variable called oneway ANOVA, whereas for two categorical independent variables called twoway ANOVA. When used at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA.^{[1],[11],[14]}
Posthoc test (multiple comparisons):
Post hoc tests (pairwise multiple comparisons) used to determine the significant pair(s) after ANOVA was found significant. Before applying posthoc test (in between subjects factors), first need to test the homogeneity of the variances among the groups (Levene's test). If variances are homogeneous (P ≥ 0.05), select any multiple comparison methods from least significant difference (LSD), Bonferroni, Tukey's, etc.^{[15],[16]} If variances are not homogeneous (P < 0.05), used to select any multiple comparison methods from GamesHowell, Tamhane's T2, etc.^{[15],[16]} Bonferroni is a good method for equal variances, whereas Tamhane's T2 for unequal variances as both calculate significance level by controlling error rate. Similarly, for repeated measures ANOVA (RMA) (in within subjects factors), select any method from LSD, Boneferroni, Sidak although Bonferroni might be a better choice. The significance level of each of the multiple comparison method is varying from other methods as each used for a particular situation.
Oneway ANOVA
The Oneway ANOVA is extension of independent samples t test (In independent samples t test used to compare the means between two independent groups, whereas in oneway ANOVA, means are compared among three or more independent groups). A significant P value of this test refers to multiple comparisons test to identify the significant pair(s).^{[17]} In this test, one continuous dependent variable and one categorical independent variable are used, where categorical variable has at least three categories. In SPSS [Analyze–compare means–oneway ANOVA].
Example: From [Table 1], 20 patient's DBP (at 30 min) are given. Oneway ANOVA test was used to compare the mean DBP in three age groups (independent variable), which was found statistically significant (p = 0.002). Levene test for homogeneity was insignificant (p = 0.231), as a result Bonferroni test was used for multiple comparisons, which showed that DBP was significantly different between two pairs i.e., age group of <30 to 30–50 and <30 to >50 (P < 0.05) but insignificant between one pair i.e., 30–50 to >50 (P > 0.05).
Twoway ANOVA
The twoway ANOVA is extension of oneway ANOVA [In oneway ANOVA, only one independent variable, whereas in twoway ANOVA, two independent variables are used]. The primary purpose of a twoway ANOVA is to understand whether there is any interrelationship between two independent variables on a dependent variable.^{[18]} In this test, a continuous dependent variable (approximately normally distributed) and two categorical independent variables are used. In SPSS [Analyze –General Linear Model –Univariate].
Example: From [Table 1], 20 patient's DBP (at 30 min) are given. Twoway ANOVA test was used to compare the mean DBP between age groups (independent variable_1) and gender (independent variable_2), which indicated that there was no significant interaction of DBP with age groups and gender (tests of BetweenSubjects effects in age groups*gender; P = 0.626) with effect size (Partial Eta Squared) of 0.065. The result also showed that there was significant difference in estimated marginal means (adjusted mean) of DBP between age groups (P = 0.005) but insignificant in gender (P = 0.662), where sex and age groups was adjusted.
Oneway repeated measures ANOVA
Repeated Measures ANOVA (RMA) is the extension of the paired t test. RMA is also referred to as withinsubjects ANOVA or ANOVA for paired samples. Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or more than two time periods. (In paired samples t test, compared the means between two dependent groups, whereas in RMA, compared the means between three or more dependent groups). Before calculating the significance level, Mauchly's test is used to assess the homogeneity of the variance (also called sphericity) within all possible pairs. When P value of Mauchly's test is insignificant (P ≥ 0.05), equal variances are assumed and P value for RMA would be taken from sphericity assumed test (Tests of WithinSubjects effects). In case variances are not homogeneous (Mauchly's test: P < 0.05), epsilon (ε) value (which shows the departure of the sphericity, 1 shows perfect sphericity) decides the statistical method to calculate P value for RMA. When ε≥0.75 HuynhFeldt while for ε<0.75, GreenhouseGeisser method (univariate method) or Wilks' lambda (multivariate method) is used to calculate P value for the RMA.^{[19]} When the RMA is significant, pairwise comparison contains multiple paired t tests with a Bonferroni correction is used.^{[20]} In SPSS [Analyze –General Linear Model – Repeated Measures ANOVA].
Example: From [Table 1], 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity indicated that variances were equal (P = 0.099) between the pairs. RMA tests (i.e., WithinSubjects effects) was assessed using sphericity assumed test (P value = 0.001), which indicated that change in DBP over the time was statistically significant. Bonferroni multiple comparisons indicated that mean difference was statistically significant between DBP_B/l to DBP_30 min and DBP_30 min to DBP_60 min (P < 0.05) but insignificant between DBP_B/l to DBP_60 min (P > 0.05).
Twoway repeated measures ANOVA
Twoway Repeated Measures ANOVA is combination of betweensubject and withinsubject factors. A twoway RMA (also known as a twofactor RMA or a twoway “Mixed ANOVA”) is extension of oneway RMA [In oneway RMA, use one dependent variable under repeated observations (normally distributed continuous variable) and one categorical independent variable (i.e., time points), whereas in twoway RMA; one additional categorical independent variable is used]. The primary purpose of twoway RMA is to understand if there is an interaction between these two categorical independent variables on the dependent variable (continuous variable). The distribution of the dependent variable in each combination of the related groups should be approximately normally distributed.^{[21]} In SPSS [Analyze–General Linear Model – Repeated Measures], where second independent variable will be included as between subjects factor.
Example: From [Table 1], 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity (P = 0.138) indicated that variances were equal between the pairs. Twoway RMA tests for interaction (i.e., WithinSubjects effects) were assessed using sphericity assumed test (DBP*gender: P value = 0.214), which indicated that there was no interaction of gender with time and associated change in DBP over the time was statistically insignificant.
Oneway ANCOVA
Oneway ANCOVA is extension of oneway ANOVA [In oneway ANOVA, do not adjust the covariate, whereas in the oneway ANCOVA; adjust at least one covariate]. Thus, the oneway ANCOVA tests find out whether the independent variable still influences the dependent variable after the influence of the covariate(s) has been removed (i.e., adjusted). In this test, one continuous dependent variable, one categorical independent variable, and at least one continuous covariate for removing its effect/adjustment are used.^{[8],[22]} In SPSS [Analyze  General Linear Model – Univariate].
Example: From [Table 1], 20 patient's DBP at 30 min are given. Oneway ANCOVA test was used to compare the mean DBP in three age groups (independent variable) after adjusting the effect of baseline DBP, which was found to be statistically significant (P = 0.021). As Levene test for homogeneity was insignificant (P = 0.601), resultant Bonferroni test was used for multiple comparisons, which showed that DBP was significantly different between one pair i.e., age group of <30 to >50 (P = 0.031) and insignificant between rest two pairs i.e., <30 to 30–50 and 30–50 to >50 (P > 0.05).
Oneway repeated measures ANOCOVA
Oneway repeated measures ANCOVA is the extension of the Oneway RMA. [In oneway RMA, we do not adjust the covariate, whereas in the oneway repeated measures ANCOVA, we adjust at least one covariate]. Thus, the Oneway repeated Measures ANCOVA is used to test whether means are still statistically equal or different after adjusting the effect of the covariate(s).^{[23],[24]} In SPSS [Analyze –General Linear Model – Repeated Measures ANOVA].
Example: From [Table 1], 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity indicated that variances were equal (P = 0.093) between the pairs. RMA tests (i.e., WithinSubjects effects) were assessed using sphericity assumed test (DBP*BMI: P value = 0.011), which indicated that change in DBP over the time was statistically significant after adjusting BMI. Bonferroni multiple comparisons indicated that mean difference was statistically significant between DBP_B/l to DBP_30 min and DBP_30 min to DBP_60 min but insignificant between DBP_B/l to DBP_60 min after adjusting BMI.
Conclusions   
Student's t test, ANOVA, and ANCOVA are the statistical methods frequently used to analyze the data. Two common things among these methods are dependent variable must be in continuous scale and normally distributed, and comparisons are made between the means. All above methods are parametric method.^{[2]} When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.
Acknowledgments
Authors would like to express their deep and sincere gratitude to Dr. Prabhat Tiwari, Professor, Department of Anaesthesiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, for his encouragement to write this article. His critical reviews and suggestions were very useful for improvement in the article.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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Correspondence Address: Prabhaker Mishra Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh India
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/aca.ACA_94_19
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Reply to “Plasma Levels of Alpha and Gamma Synucleins in Children with Autism Spectrum Disorder: Statistical Validity” 

 Sarah AlMazidi, Laila Y. AlAyadhi   Medical Principles and Practice. 2022; : 1   [Pubmed]  [DOI]   35 
Optimising the use of cell salvage in revision hip arthroplasty 

 Lucy C Walker, Emma Halliwell, Stephen W Veitch   Journal of Perioperative Practice. 2022; : 1750458922   [Pubmed]  [DOI]   36 
A Primer on Hypothesis Testing for Craniofacial Surgeons: How to Obtain Valid Conclusions From Observations and Experiments 

 Jack C. Yu, Dhairya Shukla, Daniel Linder, Atbin Doroodchi, Mouchammed Agko, Ramtin Doroodchi, Hesamoldin Khodadadi Chamgordani, Alexander Y. Lin   FACE. 2022; : 2732501622   [Pubmed]  [DOI]   37 
Psychometric analysis of the ecological dispositions of rural farming communities in South Africa: Implications for human excreta reuse in agriculture 

 Simon Gwara, Edilegnaw Wale, Alfred Odindo, Ana Delicado   PLOS Sustainability and Transformation. 2022; 1(6): e0000019   [Pubmed]  [DOI]   38 
Common Statistical Methods and Reporting of Results in Medical Research 

 Guoshuang Feng, Guoyou Qin, Tao Zhang, Zheng Chen, Yang Zhao   Cardiovascular Innovations and Applications. 2022; 6(3): 117   [Pubmed]  [DOI]   39 
Yaslanma Cinsel Bilgi ve Tutum Ölçegi: Türkçe Geçerlik ve Güvenirlik Çalismasi 

 Hasan Anil KURT, Büsra YILMAZ, Handan ALAN, Cabir ALAN   Journal of Contemporary Medicine. 2022; 12(5): 781   [Pubmed]  [DOI]   40 
The methodology of food design. Part 2. Digital nutritiology in personal food 

 A. Y. Prosekov, A. D. Vesnina, O. V. Kozlova   Theory and practice of meat processing. 2022; 6(4): 328   [Pubmed]  [DOI]   41 
Comprehensive Travel Health Education for Tour Guides: Protocol for an Exploratory Sequential Mixed Methods Research 

 Ni Made Sri Nopiyani, Pande Putu Januraga, I Md Ady Wirawan, I Made Bakta   JMIR Research Protocols. 2022; 11(5): e33840   [Pubmed]  [DOI]   42 
Naoxintong capsule delay the progression of diabetic kidney disease: A realworld cohort study 

 Yuqing Zhang, Yuehong Zhang, Cunqing Yang, Yingying Duan, Linlin Jiang, De Jin, Fengmei Lian, Xiaolin Tong   Frontiers in Endocrinology. 2022; 13   [Pubmed]  [DOI]   43 
Facial Skin Aging Stages in Chinese Females 

 Xiaoxiao Yang, Mengmeng Zhao, Yifan He, Hong Meng, Qingyang Meng, Qiaoyin Shi, Fan Yi   Frontiers in Medicine. 2022; 9   [Pubmed]  [DOI]   44 
Isolating together during COVID19: Results from the Telehealth Intervention Program for older adults 

 Harmehr Sekhon, Paola Lavin, Blanca Vacaflor, Christina Rigas, Karin Cinalioglu, ChienLin Su, Katie Bodenstein, Elena Dikaios, Allana Goodman, Florence Coulombe Raymond, Marim Ibrahim, Magnus Bein, Johanna Gruber, Jade Se, Neeti Sasi, Chesley Walsh, Rim Nazar, Cezara Hanganu, Sonia Berkani, Isabelle Royal, Alessandra Schiavetto, Karl Looper, Cyrille Launay, Emily G. McDonald, Dallas Seitz, Sanjeev Kumar, Olivier Beauchet, Bassam Khoury, Stephane Bouchard, Bruno Battistini, Pascal Fallavollita, Marc Miresco, MarieAndrée Bruneau, Ipsit Vahia, Syeda Bukhari, Soham Rej   Frontiers in Medicine. 2022; 9   [Pubmed]  [DOI]   45 
Kick proficiency and skill adaptability increase from an Australian football smallsided game intervention 

 Nathan Bonney, Paul Larkin, Kevin Ball   Frontiers in Sports and Active Living. 2022; 4   [Pubmed]  [DOI]   46 
EEG Feature Analysis Related to Situation Awareness Assessment and Discrimination 

 Chuanyan Feng, Shuang Liu, Xiaoru Wanyan, Hao Chen, Yuchen Min, Yilan Ma   Aerospace. 2022; 9(10): 546   [Pubmed]  [DOI]   47 
GenomeWide Transcriptome Analysis Reveals That Upregulated Expression of Aux/IAA Genes Is Associated with Defective Leaf Growth of the slf Mutant in Eggplant 

 Wenchao Du, Yang Lu, Shuangxia Luo, Ping Yu, Jiajia Shen, Xing Wang, Shuxin Xuan, Yanhua Wang, Jianjun Zhao, Na Li, Xueping Chen, Shuxing Shen   Agronomy. 2022; 12(11): 2647   [Pubmed]  [DOI]   48 
Surface Settlement during Tunneling: Field Observation Analysis 

 Armen Z. TerMartirosyan, Rustam H. Cherkesov, Ilya O. Isaev, Victoria V. Shishkina   Applied Sciences. 2022; 12(19): 9963   [Pubmed]  [DOI]   49 
A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID19 Mortality Prediction 

 Asif Hassan Syed, Tabrej Khan, Nashwan Alromema   Diagnostics. 2022; 12(7): 1604   [Pubmed]  [DOI]   50 
Forecasting COVID19 Epidemic Trends by Combining a Neural Network with Rt Estimation 

 Pietro Cinaglia, Mario Cannataro   Entropy. 2022; 24(7): 929   [Pubmed]  [DOI]   51 
Supportive Oligonucleotide Therapy (SOT) as a Potential Treatment for Viral Infections and Lyme Disease: Preliminary Results 

 Panagiotis Apostolou, Aggelos Iliopoulos, Georgios Beis, Ioannis Papasotiriou   Infectious Disease Reports. 2022; 14(6): 824   [Pubmed]  [DOI]   52 
BlueLightBlocking Lenses Ameliorate Structural Alterations in the Rodent Hippocampus 

 Elizebeth O. Akansha, Bang V. Bui, Shonraj B. Ganeshrao, Pugazhandhi Bakthavatchalam, Sivakumar Gopalakrishnan, Susmitha Mattam, Radhika R. Poojary, Judith S. Jathanna, Judy Jose, Nagarajan N. Theruveethi   International Journal of Environmental Research and Public Health. 2022; 19(19): 12922   [Pubmed]  [DOI]   53 
Alleviation of Severe Skin Insults Following HighDose Irradiation with Isolated Human Fetal Placental Stromal Cells 

 Boaz Adani, Eli Sapir, Evgenia Volinsky, Astar LazmiHailu, Raphael Gorodetsky   International Journal of Molecular Sciences. 2022; 23(21): 13321   [Pubmed]  [DOI]   54 
An Exploratory Study on the Validation of THUNDERS: A Process to Achieve Shared Understanding in ProblemSolving Activities 

 Vanessa AgredoDelgado, Pablo H. Ruiz, Cesar A. Collazos, Fernando Moreira   Informatics. 2022; 9(2): 39   [Pubmed]  [DOI]   55 
Dietary Intake of Polyphenols Enhances Executive/Attentional Functioning and Memory with an Improvement of the Milk Lipid Profile of Postpartum Women from Argentina 

 Agustín Ramiro Miranda, Mariela Valentina Cortez, Ana Veronica Scotta, Elio Andrés Soria   Journal of Intelligence. 2022; 10(2): 33   [Pubmed]  [DOI]   56 
Combinatorial and Proportional Task: Looking for Intuitive Strategies in Primary Education 

 Maria Ricart, Assumpta Estrada   Mathematics. 2022; 10(8): 1340   [Pubmed]  [DOI]   57 
Quantitative Analysis for the Delineation of the Subthalamic Nuclei on ThreeDimensional Stereotactic MRI Before Deep Brain Stimulation Surgery for MedicationRefractory Parkinson’s Disease 

 ChunYu Su, Alex MunChing Wong, ChihChen Chang, PoHsun Tu, Chiung Chu Chen, ChihHua Yeh   Frontiers in Human Neuroscience. 2022; 16   [Pubmed]  [DOI]   58 
Disparity of Gut Microbiota Composition Among Elite Athletes and Young Adults With Different Physical Activity Independent of Dietary Status: A Matching Study 

 Yongjin Xu, Fei Zhong, Xiaoqian Zheng, HsinYi Lai, Chunchun Wu, Cong Huang   Frontiers in Nutrition. 2022; 9   [Pubmed]  [DOI]   59 
In vitro Evaluation of Isoniazid Derivatives as Potential Agents Against DrugResistant Tuberculosis 

 Joaquim Trigo Marquês, Catarina Frazão De Faria, Marina Reis, Diana Machado, Susana Santos, Maria da Soledade Santos, Miguel Viveiros, Filomena Martins, Rodrigo F. M. De Almeida   Frontiers in Pharmacology. 2022; 13   [Pubmed]  [DOI]   60 
Identification and Functional Analysis of lncRNA by CRISPR/Cas9 During the Cotton Response to SapSucking Insect Infestation 

 Jie Zhang, Jianying Li, Sumbul Saeed, William D. Batchelor, Muna Alariqi, Qingying Meng, Fuhui Zhu, Jiawei Zou, Zhongping Xu, Huan Si, Qiongqiong Wang, Xianlong Zhang, Huaguo Zhu, Shuangxia Jin, Daojun Yuan   Frontiers in Plant Science. 2022; 13   [Pubmed]  [DOI]   61 
ShortTerm Administration of Lemon Balm Extract Ameliorates Myocardial Ischemia/Reperfusion Injury: Focus on Oxidative Stress 

 Nevena Draginic, Isidora Milosavljevic, Marijana Andjic, Jovana Jeremic, Marina Nikolic, Jasmina Sretenovic, Aleksandar Kocovic, Ivan Srejovic, Vladimir Zivkovic, Sergey Bolevich, Stefani Bolevich, Svetlana Curcic, Vladimir Jakovljevic   Pharmaceuticals. 2022; 15(7): 840   [Pubmed]  [DOI]   62 
Chitosan/Cyclodextrin Nanospheres for Potential NosetoBrain Targeting of Idebenone 

 Federica De Gaetano, Nicola d’Avanzo, Antonia Mancuso, Anna De Gaetano, Giuseppe Paladini, Francesco Caridi, Valentina Venuti, Donatella Paolino, Cinzia Anna Ventura   Pharmaceuticals. 2022; 15(10): 1206   [Pubmed]  [DOI]   63 
Ultrasensitive Functionalized PolymericNanometal Oxide Sensors for Potentiometric Determination of Ranitidine Hydrochloride 

 Eman M. Alshehri, Nawal A. Alarfaj, Salma A. AlTamimi, Maha F. ElTohamy   Polymers. 2022; 14(19): 4150   [Pubmed]  [DOI]   64 
Tax Sustainability: Tax Transparency in Latin America and the Chilean Case 

 Antonio FaúndezUgalde, Patricia ToledoZúñiga, Pedro CastroRodríguez   Sustainability. 2022; 14(4): 2107   [Pubmed]  [DOI]   65 
Impact of the COVID19 Pandemic on Citizen Travel Rules Related to Intelligent Mobility Use in Algeria 

 Yasmine Keltoum Mekhtoub, Tahar Baouni   International Journal of EPlanning Research. 2022; 11(1): 1   [Pubmed]  [DOI]   66 
How to conduct inferential statistics online: A brief handson guide for biomedical researchers 

 Shaikat Mondal, Swarup Saha, Himel Mondal, Rajesh De, Rabindranath Majumder, Koushik Saha   Indian Journal of Vascular and Endovascular Surgery. 2022; 9(1): 54   [Pubmed]  [DOI]   67 
Using search trends to analyze webbased users’ behavior profiles connected with COVID19 in mainland China: infodemiology study based on hot words and Baidu Index 

 Shuai Jiang, Changqiao You, Sheng Zhang, Fenglin Chen, Guo Peng, Jiajie Liu, Daolong Xie, Yongliang Li, Xinhong Guo   PeerJ. 2022; 10: e14343   [Pubmed]  [DOI]   68 
The effect of population distribution measures on evaluating spatial accessibility of primary healthcare institutions: A case study from China 

 Jianxia Tan, Xiuli Wang, Jay Pan   Geospatial Health. 2021; 16(1)   [Pubmed]  [DOI]   69 
Study of the Distortion of the Indirect Angular Measurements of the Calcaneus Due to Perspective: In Vitro Testing 

 Isidoro EspinosaMoyano, María ReinaBueno, Inmaculada C. PalomoToucedo, José Rafael GonzálezLópez, José Manuel CastilloLópez, Gabriel DomínguezMaldonado   Sensors. 2021; 21(8): 2585   [Pubmed]  [DOI]   70 
Highly Functionalized Modified Metal Oxides Polymeric Sensors for Potentiometric Determination of Letrozole in Commercial Oral Tablets and Biosamples 

 Ahmed Mahmoud Shawky, Maha Farouk ElTohamy   Polymers. 2021; 13(9): 1384   [Pubmed]  [DOI]   71 
Multiconstraint Spatial and Temporal Calibration of Rotating Line Structured Light Vision Sensor 

 Fuzhang Han, Qunkang Zhang, Bo Fu, Tong Yang, Yue Wang, Rong Xiong   IEEE Transactions on Instrumentation and Measurement. 2021; 70: 1   [Pubmed]  [DOI]   72 
Medicinal plant extracts protect epithelial cells from infection and DNA damage caused by colibactinproducing
Escherichia coli
, and inhibit the growth of bacteria


 T. Kaewkod, R. Tobe, Y. Tragoolpua, H. Mihara   Journal of Applied Microbiology. 2021; 130(3): 769   [Pubmed]  [DOI]   73 
“Developing Capabilities”. Inclusive Extracurricular Enrichment Programs to Improve the WellBeing of Gifted Adolescents 

 Ana María CasinoGarcía, María José LlopisBueno, María Gloria GómezVivo, Amparo JuanGrau, Tamar ShualiTrachtenberg, Lucía I. LlinaresInsa   Frontiers in Psychology. 2021; 12   [Pubmed]  [DOI]   74 
An Overview of Supervised Machine Learning Methods and Data Analysis for COVID19 Detection 

 Aurelle Tchagna Kouanou, Thomas Mih Attia, Cyrille Feudjio, Anges Fleurio Djeumo, Adèle Ngo Mouelas, Mendel Patrice Nzogang, Christian Tchito Tchapga, Daniel Tchiotsop, Sharan Srinivas   Journal of Healthcare Engineering. 2021; 2021: 1   [Pubmed]  [DOI]   75 
Effect of Partial Soybean Replacement by Shrimp ByProducts on the Productive and Economic Performances in African Catfish (Clarias lazera) Diets 

 Ibrahim S. AbuAlya, Yousef M. Alharbi, Said I. Fathalla, Ibrahim S. Zahran, Saad M. Shousha, Hassan A. AbdelRahman   Fishes. 2021; 6(4): 84   [Pubmed]  [DOI]   76 
Phenotypic Characterization and Differential Gene Expression Analysis Reveal That Dwarf Mutant dwf Dwarfism Is Associated with Gibberellin in Eggplant 

 Yang Lu, Shuangxia Luo, Qiang Li, Na Li, Wenchao Du, Ping Yu, Xing Wang, Weiwei Zhang, Shuxin Xuan, Xuan Zhou, Jiajia Shen, Jianjun Zhao, Yanhua Wang, Xueping Chen, Shuxing Shen   Horticulturae. 2021; 7(5): 114   [Pubmed]  [DOI]   77 
Candidate Gene, SmCPR1, Encoding CPR1 Related to Plant Height of the Eggplant Dwarf Mutant dwf 

 Yang Lu, Shuangxia Luo, Na Li, Qiang Li, Wenchao Du, Weiwei Zhang, Ping Yu, Shuxin Xuan, Yanhua Wang, Jianjun Zhao, Xueping Chen, Shuxing Shen   Horticulturae. 2021; 7(7): 196   [Pubmed]  [DOI]   78 
Putative Riemerella anatipestifer Outer Membrane Protein H Affects Virulence 

 Qun Gao, Shuwei Lu, Mingshu Wang, Renyong Jia, Shun Chen, Dekang Zhu, Mafeng Liu, Xinxin Zhao, Qiao Yang, Ying Wu, Shaqiu Zhang, Juan Huang, Sai Mao, Xumin Ou, Di Sun, Bin Tian, Anchun Cheng   Frontiers in Microbiology. 2021; 12   [Pubmed]  [DOI]   79 
Prevention and correction of postdecompression liver dysfunction in obstructive jaundice in experimental animals 

 M. M. Magomedov, M. A. Khamidov, H. M. Magomedov, K. I. Hajiyev   Bulletin of the Medical Institute "REAVIZ" (REHABILITATION, DOCTOR AND HEALTH). 2021; 11(4): 45   [Pubmed]  [DOI]   80 
Cryoneurolysis’ outcome on pain experience (COPE) in patients with lowback pain: study protocol for a singleblinded randomized controlled trial 

 K. Truong, K. Meier, L. Nikolajsen, M. W. van Tulder, J. C.H Sørensen, M. M Rasmussen   BMC Musculoskeletal Disorders. 2021; 22(1)   [Pubmed]  [DOI]   81 
Formulation, Evaluation, and Clinical Assessment of Novel Solid Lipid Microparticles of Tetracycline Hydrochloride for the Treatment of Periodontitis 

 Rajkiran Narkhede, Rajani Athawale, Nikita Patil, MalaDixit Baburaj   AAPS PharmSciTech. 2021; 22(5)   [Pubmed]  [DOI]   82 
Effects of mentophysical exercises on mental fatigue of shift work 

 Vahideh Mohammadi Nezhad, Hamideh Razavi, Mahdi Mohammadi Nezhad   International Journal of Occupational Safety and Ergonomics. 2021; : 1   [Pubmed]  [DOI]   83 
Mechanism of MicroRNA Regulating the Progress of Atherosclerosis in ApoEdeficient Mice 

 Xiaoqian Lou, Dawei Wang, Zehui Gu, Tengteng Li, Liqun Ren   Bioengineered. 2021;   [Pubmed]  [DOI]   84 
Effect of miRNA200a on radiosensitivity of osteosarcoma cells by targeting Bone morphogenetic protein receptor 2 

 Xian Tao, Jiansheng Cheng, Xinghua Wang   Bioengineered. 2021; 12(2): 12625   [Pubmed]  [DOI]   85 
Innovative derivative/zero ratio spectrophotometric method for simultaneous determination of sofosbuvir and ledipasvir: Application to average content and uniformity of dosage units 

 Hanan I. ELShorbagy, Fathalla Belal   Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2021; : 120623   [Pubmed]  [DOI]   86 
Spatially distributed soil losses and sediment yield: A case study of Langat watershed, Selangor, Malaysia 

 Noor Fadzilah Yusof, Tukimat Lihan, Wan Mohd Razi Idris, Zulfahmi Ali Rahman, Muzneena Ahmad Mustapha, Mohd. Abdul Wahab Yusof   Journal of Asian Earth Sciences. 2021; 212: 104742   [Pubmed]  [DOI]   87 
Matrine injection inhibits pancreatic cancer growth via modulating carbonic anhydrases a network pharmacologybased study with in vitro validation 

 Panling Xu, Chienshan Cheng, Juying Jiao, Hao Chen, Zhen Chen, Ping Li   Journal of Ethnopharmacology. 2021; : 114691   [Pubmed]  [DOI]   88 
Magna Graecia transcatheter aortic valve implantation registry: data on contrast medium osmolality and postprocedural acute kidney injury 

 Fortunato Iacovelli, Antonio Pignatelli, Alessandro Cafaro, Eugenio Stabile, Luigi Salemme, Angelo Cioppa, Armando Pucciarelli, Francesco Spione, Francesco Loizzi, Emanuela De Cillis, Vincenzo Pestrichella, Alessandro Santo Bortone, Tullio Tesorio, Gaetano Contegiacomo   Data in Brief. 2021; 35: 106827   [Pubmed]  [DOI]   89 
Analyzing wind power data using analysis of means under neutrosophic statistics 

 Muhammad Aslam   Soft Computing. 2021; 25(10): 7087   [Pubmed]  [DOI]   90 
The effects of photobiomodulation using LED on the repair process of skin graft donor sites 

 Rosadélia Malheiros Carboni, Marcela Leticia Leal Gonçalves, Elaine Marlene Tacla, Daniela Fátima Teixeira Silva, Sandra Kalil Bussadori, Kristianne Porta Santos Fernandes, Anna Carolina Ratto Tempestini Horliana, Raquel Agnelli MesquitaFerrari   Lasers in Medical Science. 2021;   [Pubmed]  [DOI]   91 
An Overview of Quality Improvement Processes and Data Analysis in Perioperative Nursing Practice 

 Shelly S. Peralta, Kari A. Dodge, Renata A. Jones   AORN Journal. 2021; 114(4): 294   [Pubmed]  [DOI]   92 
Bioequivalence and Evaluation Parameters Based on the Pharmacodynamics of Miglitol in Healthy Volunteers 

 Juan Yan, XiaoMin Li, YanXin Zhang, SuMei Xu, WanLi Liu, Jie Guo, XiaoLei Hu, Ting Zou, YuYing Xu, PingSheng Xu   Clinical Pharmacology in Drug Development. 2021; 10(6): 582   [Pubmed]  [DOI]   93 
Electrochemical Detection of Imidacloprid Using Cu–rGO Composite Nanofibers Modified Glassy Carbon Electrode 

 Soorya Srinivasan, Noel Nesakumar, John Bosco Balaguru Rayappan, Arockia Jayalatha Kulandaiswamy   Bulletin of Environmental Contamination and Toxicology. 2020; 104(4): 449   [Pubmed]  [DOI]   94 
CRMP2 is a therapeutic target that suppresses the aggressiveness of breast cancer cells by stabilizing RECK 

 Binyan Lin, Yongxu Li, Tiepeng Wang, Yangmin Qiu, Zhenzhong Chen, Kai Zhao, Na Lu   Oncogene. 2020; 39(37): 6024   [Pubmed]  [DOI]   95 
Antiparasitic effects of ethanolic extracts of Piper arboreum and Jatropha gossypiifolia leaves on cercariae and adult worms of Schistosoma mansoni 

 Rayan Rubens da Silva Alves, João Gustavo Mendes Rodrigues, Andrea TelesReis, Ranielly Araújo Nogueira, Irlla Correia Lima Licá, Maria Gabriela Sampaio Lira, Raynara da Silva Alves, Nêuton SilvaSouza, Teresinha de Jesus Aguiar dos Santos Andrade, Guilherme Silva Miranda   Parasitology. 2020; 147(14): 1689   [Pubmed]  [DOI]   96 
An EnsembleLearning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD) 

 Asif Hassan Syed, Tabrej Khan, Atif Hassan, Nashwan A. Alromema, Muhammad Binsawad, Alhuseen Omar Alsayed   IEEE Access. 2020; 8: 222126   [Pubmed]  [DOI]   97 
Statistical and MachineLearning Analyses in Nutritional Genomics Studies 

 Leila Khorraminezhad, Mickael Leclercq, Arnaud Droit, JeanFrançois Bilodeau, Iwona Rudkowska   Nutrients. 2020; 12(10): 3140   [Pubmed]  [DOI]   98 
In vitro Interactions between Streptococcus intermedius and Streptococcus salivarius K12 on a Titanium Cylindrical Surface 

 Carla Vacca, Maria Paola Contu, Cecilia Rossi, Maria Laura Ferrando, Cornelio Blus, Serge SzmuklerMoncler, Alessandra Scano, Germano Orrù   Pathogens. 2020; 9(12): 1069   [Pubmed]  [DOI]   99 
circFAT1(e2) Promotes Papillary Thyroid Cancer Proliferation, Migration, and Invasion via the miRNA873/ZEB1 Axis 

 Jiazhe Liu, Hongchang Li, Chuanchao Wei, Junbin Ding, Jingfeng Lu, Gaofeng Pan, Anwei Mao, Tao Huang   Computational and Mathematical Methods in Medicine. 2020; 2020: 1   [Pubmed]  [DOI]   100 
Organizational Governance, Social Bonds and Information Security Policy Compliance: A Perspective towards Oil and Gas Employees 

 Rao Faizan Ali, P.D.D. Dominic, Kashif Ali   Sustainability. 2020; 12(20): 8576   [Pubmed]  [DOI]   101 
The Accuracy of RealTime hmF2 Estimation from Ionosondes 

 Carlo Scotto, Dario Sabbagh   Remote Sensing. 2020; 12(17): 2671   [Pubmed]  [DOI]  






