Calibration transfer between NIR instruments using optimally predictive calibration subsets.

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Comparison of six machine learning methods for differentiating benign and malignant thyroid nodules using ultrasonographic characteristics.

BACKGROUND: Several machine learning (ML) classifiers for thyroid nodule diagnosis have been compared in terms of their accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and area under the receiver operating curve (AUC). A total of 525 patients with thyroid nodules (malignant, n = 228; benign, n = 297) underwent conventional ultrasonography, strain elastography, and contrast-enhanced ultrasound. Six algorithms were compared: support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), logistic regression (LG), GlmNet, and K-nearest neighbors (K-NN). The diagnostic performances of the 13 suspicious sonographic features for discriminating benign and malignant thyroid nodules were assessed using different ML algorithms. To compare these algorithms, a 10-fold cross-validation paired t-test was applied to the algorithm performance differences. RESULTS: The logistic regression algorithm had better diagnostic performance than the other ML algorithms. However, it was only slightly higher than those of GlmNet, LDA, and RF. The accuracy, sensitivity, specificity, NPV, PPV, and AUC obtained by running logistic regression were 86.48%, 83.33%, 88.89%, 87.42%, 85.20%, and 92.84%, respectively. CONCLUSIONS: The experimental results indicate that GlmNet, SVM, LDA, LG, K-NN, and RF exhibit slight differences in classification performance.

Aluminum(III) triggered aggregation-induced emission of glutathione-capped copper nanoclusters as a fluorescent probe for creatinine.

Glutathione-capped copper nanoclusters (CuNCs) are presented that display aggregation-induced emission (AIE). This feature was exploited for selective and sensitive quantification of creatinine (CRN) which is an important diagnostic parameter. In the presence of Al3+ ions, such CuNCs rapidly aggregate, and this induces enhanced a red emission. The AIE nature of CuNCs was proven via TEM and fluorimetry. On addition of CRN, the coordination between CRN and Al3+ ions led to the quenching of fluorescence due to weakening the AIE. The best fluorescence intensity was measured at excitation/emission peaks of 360/585 nm. Quenched fluorescence intensity showed a linear dependence on the concentrations of CRN in the range of 2.5-34 µgL-1 with a detection limit of 0.63 µgL-1. The sensing mechanism of probe for CRN detection is discussed. The probe was applied to the determination of CRN in spiked human serum samples and gave satisfactory results. Graphical abstract Aluminum(III) ions trigger the aggregation-induced emission (AIE) in glutathione-capped copper nanoclusters (CuNCs). Introduction of creatinine leads to deaggregation of CuNC aggregates, and this is accompanied by the quenching of the luminescence. This phenomenon was exploited in an assay for creatinine.

Two-stage test of means of unordered pairs.

The problem of testing equality of means of a bivariate normal distribution on the basis of a sample of size n has been considered when the labels of the observations are either missing or not known. The problem may arise in many applied settings, especially in genetics. Classical likelihood ratio test fails here because of identifiability problems. We propose a two-stage testing procedure using a recently developed test in the context of penalized splines. The proposed testing procedure is found to outperform the tests proposed in the literature. Copyright © 2017 John Wiley & Sons, Ltd.

Effectiveness of Air Drying and Magnification Methods for Detecting Initial Caries on Occlusal Surfaces Using Three Different Diagnostic Aids.

Objective-The aim of this study was to assess the effect of magnification and air-drying on detection of carious lesion. Study Design-44 human extracted premolars were selected with sound occlusal surfaces without frank cavitation. The Diagnostic techniques used were Unaided visual examination, Magnifying Loupes (4.2×) and Stereomicroscope (10×, before and after air-drying) and then the teeth were sectioned bucco-lingually and both the surfaces were examined under Stereomicroscope (50×) to assess the presence or absence of carious lesion in the pit and fissures. The scores were compared to obtain Cohen's kappa coefficient (Reproducibility) and subjected to the Friedman Test and Paired t test. Sensitivity, specificity and positive predictive value used to assess accuracy. Results-On Statistical analysis, visual examination before and after air drying had highest specificity but lowest sensitivity compared to different diagnostic techniques. Magnifying loupes after air-drying had highest sensitivity and lowest specificity compared to other diagnostic techniques. Conclusion-Air drying combined with magnifying aids are cost-effective, reliable method for detection of early carious lesion. If used in pediatric clinical practice, any undesirable pain and discomfort to the patient due to invasive procedures and helps in employing preventive measures.

Use of a Smartphone-Based Medication Adherence Platform to Improve Outcomes in Uncontrolled Type 2 Diabetes Among Veterans: Prospective Case-Crossover Study.

BACKGROUND: Medication nonadherence is a problem that impacts both the patient and the health system. OBJECTIVE: The objective of this study was to evaluate the impact of a novel smartphone app with patient-response-directed clinical intervention on medication adherence and blood glucose control in noninsulin-dependent patients with type 2 diabetes mellitus (T2DM). METHODS: We enrolled 50 participants with T2DM not on insulin with smartphones from a rural health care center in Northern Nevada for participation in this case-crossover study. Participants underwent a standard of care arm and an intervention arm. Each study arm was 3 months long, for a total of 6 months of follow-up. Participants had a hemoglobin A1c (HbA1c) lab draw at enrollment, 3 months, and 6 months. Participants had monthly "medication adherence scores" (MAS) and "Self-Efficacy for Appropriate Medication Use Scale" (SEAMS) questionnaires completed at baseline and monthly for the duration of the study. Our primary outcomes of interest were the changes in HbA1c between study arms. Secondary outcomes included the evaluation of the difference in the proportion of participants achieving a clinically meaningful reduction in HbA1c and the difference in the number of participants requiring diabetes therapy escalation between study arms. Exploratory outcomes included the analysis of the variation in medication possession ratio (MPR), MAS, and SEAMS during each study arm. RESULTS: A total of 30 participants completed both study arms and were included in the analysis. Dropouts were higher in participants enrolled in the standard of care arm first (9/25, 36% vs 4/25, 16%). Participants had a median HbA1c of 9.1%, had been living with T2DM for 6 years, had a median age of 66 years, and had a median of 8.5 medications. HbA1c reduction was 0.69% in the intervention arm versus 0.35% in the standard of care arm (P=.30). A total of 70% (21/30) of participants achieved a clinically meaningful reduction in HbA1c of 0.5% in the app intervention arm versus 40% (12/30) in the standard of care arm (odds ratio 2.29, 95% CI 0.94-5.6; P=.09). Participants had higher odds of a therapy escalation while in the standard of care arm (18/30, 60% vs 5/30, 16.7%, odds ratio 4.3, 95% CI 1.2-15.2; P=.02). The median MPR prior to enrollment was 109%, 112% during the study's intervention arm, and 102% during the standard of care arm. The median real-time MAS was 93.2%. The change in MAS (1 vs -0.1; P=.02) and SEAMS (1.9 vs -0.2; P<.001) from baseline to month 3 was higher in the intervention arm compared to standard of care. CONCLUSIONS: A novel smartphone app with patient-response-directed provider intervention holds promise in the ability to improve blood glucose control in complex non-insulin-dependent T2DM and is worthy of additional study.

Improved modelling for low-correlated multiple responses by common-subset-of-independent-variables partial-least-squares.

In this study, a new approach for PLS modelling for low-correlated multiple responses, called Common-Subset-of-Independent-Variables Partial-Least-Squares, denoted as CSIV-PLS1, is proposed and evaluated. In CSIV-PLS1, for each response vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG. CSIV-PLS1 combines a common variable set for all response vectors, which is a characteristic of PLS2, with the individual model complexity for each response, which is a characteristic of PLS1. These characteristics make CSIV-PLS1 more flexible than PLS2. The selective and predictive abilities of the proposed CSIV-PLS1 method are investigated using one simulated and four real data sets with low-correlated multiple responses from different sources. The simulated data set is used to test the general applicability of the CSIV-PLS1 method. The predictive abilities, measured by the RMSEP values, resulting from CSIV-PLS1 models, are statistically compared with those of the corresponding PLS1 and PLS2 models, using one-tailed paired t-tests. The selective ability of the CSIV-PLS1 method is good, because mostly variables with an informative meaning to the responses are selected. The RMSEP values resulting from the CSIV-PLS1 method are (i) significantly lower at the 95% confidence level than those of the corresponding PLS2 method, and (ii) borderline significantly lower at the 90-95% confidence level than those of the corresponding PLS1 methods. In case of low-correlated multiple responses, the predictive ability of the CSIV-PLS1 method is significantly better than that of the PLS2 method, and borderline significantly better than those of the corresponding PLS1 methods. Therefore, CSIV-PLS1 modelling may be an alternative for PLS1 or PLS2.

Population-based normative data for the Scoliosis Research Society 22r, EQ-5D, and VAS questionnaires among individuals aged 20 to 69 years.

AIMS: To provide normative data that can assess spinal-related disability and the prevalence of back or leg pain among adults with no spinal conditions in the UK using validated questionnaires. METHODS: A total of 1,000 participants with equal sex distribution were included and categorized in five age groups: 20 to 29, 30 to 39, 40 to 49, 50 to 59, and 60 to 69 years. Individuals with spinal pathologies were excluded. Participants completed the Scoliosis Research Society-22 (SRS-22r), visual analogue scale (VAS) for back/leg pain, and the EuroQol five-dimension index (EQ-5D/VAS) questionnaires, and disclosed their age, sex, and occupation. They were also categorized in five professional groups: doctors, nurses, allied health professionals, office workers, and manual workers. RESULTS: The mean age of all participants was 43.8 years (20 to 69). There was no difference in the SRS-22r, EQ-5D, or VAS scores among male and female participants (p > 0.05). There was incremental decrease in SRS-22r total scores as the age increased. The mean EQ-5D index score (0.84) ranged little across the age groups (0.72 to 0.91) but reduced gradually with increasing age. There was difference between the SRS-22r total score (4.51), the individual domain scores, and the EQ-5D score (index: 0.94 and VAS: 89) for the doctors' group compared to all other occupational categories (p < 0.001). Doctors had a younger mean age of participants, which may explain their improved spinal health. There was no difference in the total or sub-domain SRS-22r and EQ-5D scores between the other four occupational groups. CONCLUSION: This study provides the first normative data for the SRS-22r, EQ-5D, and VAS for back/leg pain questionnaires among adults in the UK. We recorded an excellent correlation between the three assessment tools with individuals who reported less back and leg pain having better quality of life and greater function. The participants' age, rather than their sex or profession, appears to be the major determinant for spinal health and quality of life. Cite this article: Bone Jt Open 2022;3(2):130-134.

Importance of Paired t-test in Time-based Comparison of Obturation and SealBio Techniques in Root Canal Treatment.

How to cite this article: Ozcelik F, Ersahan S. Importance of Paired t-test in Time-based Comparison of Obturation and SealBio Techniques in Root Canal Treatment. Int J Clin Pediatr Dent 2021;14(1):40.

Monitoring and evaluating the spatiotemporal variations of the water quality of a stretch of the Bhagirathi-Hugli River, West Bengal, India, using geospatial technology and integrated statistical methods.

Water quality is a critical environmental issue because all forms of life depend on the water. The present study primarily focused on the spatiotemporal trends of water quality in a section of the Bhagirathi-Hugli River, West Bengal, using geospatial technology and integrated statistical methods. For this purpose, 83 samples of 7 water parameters were analysed and compared them with Indian Standards (IS 2004), Environmental Protection Agency (EPA 2001) and World Health Organization (WHO 1993) for the protection of aquatic life and human consumption. Correlation, box and whisker plots, paired sample t test, water quality index (WQI), cluster analysis (CA) and principal component analysis (PCA) were applied as an integrated multivariate statistical approach to understanding the nature of water quality. Pollution sources were identified by PCA indicating different origins both naturally and anthropogenic sources. The box and whisker plots displayed the significantly spatiotemporal variations and concentration of the variables. The paired sample t test identified that the surface water quality varied significantly between the seasons with significant value p < 0.05. Cluster analysis grouped 83 monitoring sites into 4 clusters to identify the pollution status such as low, moderate, high and very high pollution sites. Principal component analysis confirmed that the first three PCs with eigenvalues are higher than 1 contributing 90.83% of total variability for various parameters. The conductivity, total dissolved solids (TDS), salt and pH were expressively influenced by the anthropogenic effect while the temperature, oxidation-reduction potential (ORP) and dissolved oxygen (DO) were affected by seasonal factors. Results of WQI ranged from 45.04 to 83.79, and an average value was 69.55 with 69% samples representing poor water quality for drinking and domestic purposes. It also indicates that the water quality of rural sites was better than industrial and urban sites in both seasons and also shows that it was better for the duration of the post-monsoon than pre-monsoon.

The Health Sciences and Technology Academy: Re-Imagining Programmatic Delivery During the COVID-19 Pandemic.

Out-of-school time academic/STEM programs provide educational enrichment to a myriad of student populations with some designed to assist those underrepresented and at-risk who desire to purse post-secondary studies. One such program in West Virginia (WV) is the Health Sciences and Technology Academy (HSTA), which provides hands-on, in person and engaging educational enrichment with the intent to increase the college going rates of Appalachia's most vulnerable youth. In March 2020, HSTA key personnel encountered the task of redesigning program delivery due to the immediate shutdown of all in person operations resulting from the novel coronavirus, SARS-CoV-19 (COVID-19) pandemic. This paper discusses the programmatic changes of program delivery during HSTA's key program components-the Summer Institute and the Community Based Programming. This paper also utilizes repeated measures one-way multivariate analysis (MANOVA) and paired t-test analysis to examine participants' perspectives on learning, enjoyment and satisfaction of the aforementioned components prior to and during the COVID-19 pandemic.

Improved multi-class discrimination by Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis.

In this study, a new PLS-DA modelling approach for multi-class discriminant analysis, called Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis is proposed and evaluated. Because in this method Partial-Least-Squares models for one component are used, it is denoted as CSIV-PLS1-DA. In this method for each class vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG. CSIV-PLS1-DA combines a common variable set for all class vectors, which is a characteristic of PLS2-DA, with the individual model complexity for each class vector, which is a characteristic of PLS1-DA. These characteristics make CSIV-PLS1-DA more flexible than PLS2-DA. CSIV-PLS1-DA is found to be an alternative for PLS1-DA or PLS2-DA when the correlations between the responses are low, which is often the case in discriminant analysis. The performance of the CSIV-PLS1-DA method is investigated using one simulated and eight real multi-class data sets from different sources. The classification abilities, measured by the percentage classification accuracy rates (%Acc), resulting from CSIV-PLS1-DA, are statistically compared with those of PLS1-DA and PLS2-DA, using one-tailed paired t-tests at the 95% confidence level. The results show that the %Acc values resulting from the CSIV-PLS1-DA method are significantly higher than those of the corresponding PLS1-DA and PLS2-DA methods, meaning that the classification ability of the CSIV-PLS1-DA method is significantly better.

Oral tranexamic acid for an additional 24 hours postoperatively versus a single preoperative intravenous dose for reducing blood loss in total knee arthroplasty: results of a randomized controlled trial (TRAC-24).

AIMS: In total knee arthroplasty (TKA), blood loss continues internally after surgery is complete. Typically, the total loss over 48 postoperative hours can be around 1,300 ml, with most occurring within the first 24 hours. We hypothesize that the full potential of tranexamic acid (TXA) to decrease TKA blood loss has not yet been harnessed because it is rarely used beyond the intraoperative period, and is usually withheld from 'high-risk' patients with a history of thromboembolic, cardiovascular, or cerebrovascular disease, a patient group who would benefit greatly from a reduced blood loss. METHODS: TRAC-24 was a prospective, phase IV, single-centre, open label, parallel group, randomized controlled trial on patients undergoing TKA, including those labelled as high-risk. The primary outcome was indirect calculated blood loss (IBL) at 48 hours. Group 1 received 1 g intravenous (IV) TXA at the time of surgery and an additional 24-hour postoperative oral regime of four 1 g doses, while Group 2 only received the intraoperative dose and Group 3 did not receive any TXA. RESULTS: Between July 2016 and July 2018, 552 patients were randomized to either Group 1 (n = 241), Group 2 (n = 243), or Group 3 (n = 68), and 551 were included in the final analysis. The blood loss did differ significantly between the two intervention groups (733.5 ml (SD 384.0) for Group 1 and 859.2 ml (SD 363.6 ml) for Group 2; mean difference -125.8 ml (95% confidence interval -194.0 to -57.5; p < 0.001). No differences in mortality or thromboembolic events were observed in any group. CONCLUSION: These data support the hypothesis that in TKA, a TXA regime consisting of IV 1 g perioperatively and four oral 1 g doses over 24 hours postoperatively significantly reduces blood loss beyond that achieved with a single IV 1 g perioperative dose alone. TXA appears safe in patients with history of thromboembolic, cardiovascular, and cerebrovascular disease. Cite this article: Bone Joint J 2021;103-B(10):1595-1603.

Statistical Treatment of Clinical Investigations in Pediatric Cardiology.

This paper describes various statistical methods used by the author during multiple studies conducted by the author. Initially, the data were scrutinized to ensure normal distribution, and expressed as mean ± standard deviation (SD) or standard error of mean (SEM) for normally distributed variables. Medians and ranges were given for the data with skewed distribution. Two tailed, paired t tests or independent sample t tests (analysis of variance) were used for normally distributed data, while non-parametric chi-square or similar other tests were utilized for data with skewed distribution. Statistical significance was set at a p value of < 0.05. Bonferroni correction was applied when the study involves multiple comparisons. A number of other statistical methods used during these studies were also discussed. Finally, special methods used in evaluating aortic remodeling subsequent to balloon angioplasty of native aortic coarctation were reviewed.

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectrum of EEG data related to motor imagery, the shared features across sessions or across subjects, namely, the mean and variance of model parameters, are extracted. Then, select the data sets that were most relevant to the new data set according to Euclidean distance to update the shared features. Finally, utilize the shared features and subject/session-specific features jointly to generate a new model. We evaluated our algorithm by analyzing the motor imagery EEG data from 10 healthy participants and a public data set from BCI competition IV. The classification accuracy of the proposed transfer learning is higher than that of traditional machine learning algorithms. The results of the paired t test showed that the classification results of PSD and the transfer learning algorithm were significantly different (p = 2.0946e-9), and the classification results of CSP and the transfer learning algorithm were significantly different (p = 1.9122e-6). The test accuracy of data set 2a of BCI competition IV was 85.7% ± 5.4%, which was higher than that of related traditional machine learning algorithms. Preliminary results suggested that the proposed algorithm can be effectively applied to the classification of motor imagery EEG signals across sessions and across subjects and the performance is better than that of the traditional machine learning algorithms. It can be promising to be applied to the field of brain-computer interface (BCI). Graphical abstract.

Waste incineration of Polytetrafluoroethylene (PTFE) to evaluate potential formation of per- and Poly-Fluorinated Alkyl Substances (PFAS) in flue gas.

In recent years, concerns over some per- and polyfluorinated alkyl substances (PFAS) have grown steadily. PFAS are a large group of chemical substances with widely differing properties. While one class of PFAS, fluoropolymers, have been demonstrated to meet the OECD criteria for polymers of low concern during the in use phase of their lifecycle, questions remain regarding waste handling at the end of useful life for products containing fluoropolymers. To show that polytetrafluoroethylene (PTFE) can be almost fully transformed into fluorine (F) (as hydrofluoric acid (HF)) and to study the possible generation of low molecular weight per- and polyfluorinated alkyl substances (PFAS), PTFE combustion under typical waste incineration conditions at the BRENDA (German acronym for "Brennkammer mit Dampfkessel") pilot plant at Karlsruhe Institute of Technology (KIT) was investigated. Results indicate that, within procedural quantitation limits, no statistically significant evidence was found that the PFAS studied were created during the incineration of PTFE. Therefore, municipal incineration of PTFE using best available technologies (BAT) is not a significant source of the studied PFAS and should be considered an acceptable form of waste treatment.

Impact of blue green algae (BGA) technology: an empirical evidence from northwestern Indo-Gangetic Plains.

The focus of the current study is to assess the impact of blue green algae (BGA) technology on farming practices and economic conditions of farming households. The states of Punjab, Uttar Pradesh and Haryana considered as major rice-growing states of India were chosen for the primary survey. It was observed that use of BGA resulted in 25.2% of urea reduction with an overall 3.8% increase in the yield and a marginal decrease in per acre cultivation cost. Tobit model has been used to identify the determinants of increasing cropping area under BGA. Factors such as age, education, operated land holdings and leased-in land have a positive and significant impact on area under BGA. The study also provided the comparative analysis on yield of paddy, urea consumption and income with and without BGA application. It was observed that farmers earned about 3% greater income along with 41.1% reduction in dosage of urea while reaping 1% higher yield of paddy.

The Evaluation and Clinical Efficiency of Power Scope: An Original Research.

AIMS AND OBJECTIVES: Managing mild-to-moderate Class II malocclusion is one of the common and major challenges to orthodontists. Class II discrepancies with mandibular deficiency during active growth are usually treated using myofunctional appliances. Fixed functional appliances evolved due to the noncompliance with conventional myofunctional appliances. One of the latest Class II correctors is the PowerScope appliance. The purpose of this study was to determine the amount, time, and rate of molar correction and efficacy of PowerScope. MATERIALS AND METHODS: A total of 10 participants, between 15 and 19 years' age group (mean = 16.8 years; 5 males and 5 females), requiring treatment of Class II malocclusion were considered for this study. All routine records were made. After initial leveling and alignment, lateral cephalogram was taken in standardized natural head position using Planmeca ProMax unit. Later, PowerScope was installed and a patient was monitored every month for further adjustment and reactivation. On achieving Class I molar relation, skeletal, dental, and soft-tissue linear and angular parameters were measured using Dolphin Imaging Software. Amount, rate, and total treatment time for molar correction were measured. Molar correction was calculated by taking S vertical as reference plane. The obtained values were statistically analyzed using paired t-test. RESULTS: There were statistically significant changes seen in dentoalveolar parameters such as lower incisor proclination, mandibular molar advancement, and reduction in both overjet and overbite. In skeletal parameters, due to the anterior positioning of the mandible, sagittal parameters showed statistically significant changes. In the soft tissue, a significant improvement in facial profile was seen, due to the anterior movement of soft-tissue pogonion. CONCLUSION: The results of this study have shown that statistically significant differences were found in dentoalveolar, soft-tissue, and skeletal parameters.

Comparative study on alveolar bone healing in postextraction socket versus healing aided with autologous platelet-rich fibrin following surgical removal of bilateral mandibular impacted third molar tooth: A radiographic evaluation.

AIM: This study was to evaluate and compare the bone regeneration potential of autologous platelet-rich fibrin (PRF) placed in one of the extracted sockets after the surgical removal of bilateral impacted mandibular third molars. PATIENTS AND METHODS: Twenty-five patients (10 females and 15 males; 18-35 years old) were taken for surgical removal of bilateral impacted mandibular third molar, performed in the same session. The autologous PRF was placed in one of the extracted sockets whereas the opposite side was taken as control side, and primary closure was done. Radiographic examination with orthopantomogram was done preoperatively and 1 month, 3 months, and 6 months postoperatively to assess the degree of bone regeneration at the extracted site and compare it with the control side using MATLAB software and the data are statistically analyzed using paired t-test. RESULTS: PRF side had better healing and bone formation when compared with the control side as indicated by significant P values of (P = 0.06>5%) 1 month, (P = 0.00<1%) 3 month, and (P = 0.00<1%) 6 month postoperatively. The repeated-measures ANOVA showed a significant difference seen on 1st, 3rd, and 6th months postoperatively on PRF side (P = 0.001). CONCLUSION: The autologous PRF improves and fastens the bone regeneration and healing in the extracted sockets.

Evaluation of Approaches to Analyzing Continuous Correlated Eye Data When Sample Size Is Small.

PURPOSE: To evaluate the performance of commonly used statistical methods for analyzing continuous correlated eye data when sample size is small. METHODS: We simulated correlated continuous data from two designs: (1) two eyes of a subject in two comparison groups; (2) two eyes of a subject in the same comparison group, under various sample size (5-50), inter-eye correlation (0-0.75) and effect size (0-0.8). Simulated data were analyzed using paired t-test, two sample t-test, Wald test and score test using the generalized estimating equations (GEE) and F-test using linear mixed effects model (LMM). We compared type I error rates and statistical powers, and demonstrated analysis approaches through analyzing two real datasets. RESULTS: In design 1, paired t-test and LMM perform better than GEE, with nominal type 1 error rate and higher statistical power. In design 2, no test performs uniformly well: two sample t-test (average of two eyes or a random eye) achieves better control of type I error but yields lower statistical power. In both designs, the GEE Wald test inflates type I error rate and GEE score test has lower power. CONCLUSION: When sample size is small, some commonly used statistical methods do not perform well. Paired t-test and LMM perform best when two eyes of a subject are in two different comparison groups, and t-test using the average of two eyes performs best when the two eyes are in the same comparison group. When selecting the appropriate analysis approach the study design should be considered.