Methods of Determining Sample Sizes. Sample Size Determination and Power. Wiley; 2013. pp. 17-55. [DOI: 10.1002/9781118439241.ch2]

Can milk replacer allowance affect animal performance, body development, metabolism, and skeletal muscle hypertrophy in pre-weaned dairy kids?

We aimed to evaluate performance, body development, metabolism, and expression of genes related to skeletal muscle hypertrophy in non-castrated male dairy kids fed with different levels of MR during the pre-weaning period. Sixty newborn male kids, not castrated, from Saanen and Swiss Alpine breeds, with an average body weight (BW) of 3.834 ± 0.612 kg, were distributed in a randomized block design. Breeds were the block factor in the model (random effect). Kids were allocated into 2 nutrition plans (n = 30 kids per treatment) categorized as follows: low nutritional plan (LNP; 1L MR/kid/day) or high nutritional plan (HNP; 2L MR/kid/day). All kids were harvested at 45 d of life. The majority of nitrogen balance variables were affected by the nutritional plan (P < 0.050). Morphometric measures and body condition score (2.99 - LNP vs. 3.28 - HNP) were affected by nutritional plan (P < 0.050), except hip height, thoracic depth and hip width. The nutritional plan affected the body components (P < 0.050), except esophagus and trachea. Animal performance and carcass traits were influenced by nutritional plan (P < 0.050), except carcass dressing (48.56% on average). Nutritional plan affected (P < 0.050) some blood profile variables as the total cholesterol (141.35 vs. 113.25 mg/dL), triglycerides (60.53 vs. 89.05 mg/dL), LDL (79.76 vs. 33.66 g/mL) and IGF-1 (17.77 vs. 38.55 ng/mL) for LNP and HNP respectively. Hypertrophy was greater in HNP than LNP animals (P < 0.050), being represented by the proportion of sarcoplasm (39.76 vs. 31.99%). LNP had a greater mTOR abundance than HNP (P = 0.045), but AMPK was not affected by the nutritional plan. Our findings show that a higher milk replacer allowance enhances animal performance, body development, metabolic parameters, and cellular hypertrophy in pre-weaned dairy kids.

Elective full pulpotomy in mature permanent teeth diagnosed with symptomatic irreversible pulpitis: a two years retrospective study

AIM

To investigate the outcome of elective full pulpotomy, using calcium silicate-based cements (CSBC), after 2 years, in symptomatic mature permanent teeth with carious lesions, diagnosed as irreversible pulpitis, and analyse the capacity of Wolters et al. (2017) classification to predict the likelihood of treatment failure.

METHODS

The treatment records of 56 patients with symptomatic mature teeth with carious lesions, diagnosed as irreversible pulpitis and treated by elective full pulpotomy, using CSBCs as pulp capping materials, were reviewed. Thirteen teeth were excluded. The remaining 43 teeth were evaluated retrospectively at 24 months. Fisher`s exact test with the Lancaster's mid-P adjustment was used to assess different outcomes amongst the diagnostic categories.

RESULTS

Four of the cases failed before 24 months and required root canal treatment (RCT). Overall success rate at 2 years was 90.7% (39 of 43). An inverse, but non-significant, correlation was observed between the severity of pulpitis according to the Wolters classification and the treatment success rate (p > 0.05). The type of CSBC used was associated to the success rate (OR = 10.5; 95% C.I. = 0.5 - 207.4; p = 0.027), being 82% with Endosequence and 100% with Biodentine. Postoperative pain associated significantly to lower success rate (66.7%) (Odds ratio = 8.0; 95% C.I. = 0.7 - 95.9; p = 0.047).

CONCLUSIONS

Elective full pulpotomy using a CSBC was a successful choice for the treatment of mature permanent teeth with symptoms indicative of irreversible pulpitis. There were no significant differences between the success rate of mild, moderate and severe pulpitis. Postoperative pain could be considered a risk marker for failure of full pulpotomy. The term "irreversible pulpitis" should be re-signified to indicate the need for access to the pulp chamber, rather than an indication for extraction or RCT.

A figure of merit that includes 5 distinct performance indicators to improve research evaluation of academic scholars'

The h index has become a widely known indicator to assess the research impact of academic scholars. However, its application has been associated with some criticism regarding its ability to fully capture the quality and significance of an author's research contributions. In this paper, we present a novel approach to improve the evaluation of authors' publications by means of a Figure-of-Merit (FOM) that includes 5 distinct indicators, of which, an enhanced version of the h index. Named the Enhanced Research Quality Index (ERQI), it addresses the current limitations of existing solutions and offers a more comprehensive evaluation of research quality. The ERQI builds upon the concept that one metric is never sufficient to capture the performance of an academic scholar, while multiple ones are complex to handle and interpret. The proposed ERQI considers the total number of citations, papers and co-authors and can further differentiate researchers with equal h index. By incorporating measurable, and quantitative metrics, ERQI moves away from subjective and indirect factors such as journal reputation, citation context, citation patterns and self-citation righteousness, to offer a more nuanced and accurate representation of research quality. To demonstrate the effectiveness of the proposed metric, we conducted a comparative study using a real dataset of 31 researchers in one of the top 3 engineering faculties in Lebanon, and a randomly generated dataset of 1000 author profiles with >1 million citations. Our findings indicate that ERQI provides a more balanced assessment of research quality by reducing the shortcomings of one indicator. Furthermore, it exhibits a multidimensional effect that captures more efficiently the intrinsic value of scholarly contributions. By adopting ERQI, institutions can make informed decisions that recognize both the quantity and quality of an author's research output and can obtain insightful evaluation enabling fairer recognition of academic scholars' impact and innovation.

Gender-based food intake stereotype scale (GBFISS) for adolescents: development and psychometric evaluation

Objective. The study aimed to develop and test the validity and reliability of a gender-based food intake stereotype scale (GBFISS) to further the understanding of gender stereotype influences on food intake. Design. Two cross-sectional studies were conducted among adolescents. In the first one (n = 611), exploratory and confirmatory factor analyses were performed on subsamples to identify and cross-validate the scale's structure. Evidence of concurrent validity (correlation with sexism) was also examined. In the second study (n = 813), confirmatory factor analysis was conducted to confirm the scale's dimensionality on a different sample. Further evidence of construct validity (correlations with food intake and social desirability) was examined. Invariance was tested for different features as well. Main outcome. The Gender-Based Food Intake Stereotype Scale. Results. Factor analyses on the first and second studies helped identify and confirm the GBFISS as a three-dimensional scale. The studies also provided evidence of construct validity. Support for invariance by gender and age was found, and reliability was acceptable. Conclusion. The evidence suggests that the GBFISS is valid and reliable. Further research is recommended. The contribution of gender stereotypes, as measured by the GBFISS, to well-established health behavior models should be examined.

Prevalence and Risk-Factors of Musculoskeletal Disorders Among Provincial High School Teachers in the Philippines

Pain in the muscles and tendons is one of the most common complaints among teachers. The objective of this study was to investigate the prevalence of musculoskeletal disorders (MSDs) and their risk factors among public school teachers in the Philippines. It involved 200 public school teachers in the secondary level from Calbayog City division, Samar Province, Philippines, and used an English version of the Korean Occupational Safety and Health Agency's questionnaire on MSDs for an analysis of musculoskeletal symptoms. The researchers employed frequency and percentages computations to determine the prevalence of MSDs in the respondents. Chi-square test and logistic regression were utilized to compute the correlation among socio-demographic profiles, teaching variables and MSDs. The reported overall prevalence of musculoskeletal pain was 74.5%, with legs (56.5%) and lower back (56%) having the highest prevalence. Significant differences in the prevalence of MSDs were found between age-groups (P = 0.032) and salary-groups (P = 0.045). Musculoskeletal disorders were prevalent among secondary public school teachers in the Philippines, suggesting that school administrators, curriculum and policy makers, and other stakeholders should improve the working conditions of teachers.

Revisiting the Relationship Between Correlation Coefficient, Confidence Level, and Sample Size

In the field of computational chemistry, it is a very common task to compare the predictive power of theoretical models with Pearson correlation coefficients. A general understanding is that larger sample sizes lead to increased precision. However, what is the minimum sample size required for comparing two models? This issue has not been well addressed in this field. To the best of our knowledge, the only serious study of this kind was published by Carlson in 2013 [ J. Chem. Inf. Model. 2013 , 53 1837 - 1841 ], where they proposed a method for estimating the minimum sample size required by this task. Considering how a benchmark comparison is conducted in reality, we want to point out that (i) the possible intercorrelation between two models should not be neglected and (ii) the one-sided test is more reasonable because comparison direction is known a priori. Carlson's method has significantly overestimated the required minimum sample size due to these two issues. Here, we will describe a more appropriate method based on Dunn and Clark's test statistic, and we have designed an extensive numerical test to validate our method. The minimum sample sizes required by comparing two models under various conditions are computed with our method. Our study has shown that the required minimum sample size is determined by several factors, including confidence, power, correlation coefficients as well as the intercorrelation between two models. As a rule of thumb, a couple of hundred samples are sufficient at 90% confidence or above for comparing two models producing meaningful R values.