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Introduction: Apert, Crouzon, and Pfeiffer syndromes are common genetic syndromes related to syndromic craniosynostosis (SC), whereby it is a congenital defect that occurs when the cranial growth is distorted. Identifying cranial angles associated with these 3 syndromes may assist the surgical team to focus on a specific cranial part during the intervention planning, thus optimizing surgical outcomes and reducing potential morbidity., Objective: The aim of this study is to identify the cranial angles, which are associated with Apert, Crouzon, and Pfeiffer syndromes., Methods: The cranial computed tomography scan images of 17 patients with SC and 22 control groups aged 0 to 12 years who were treated in the University Malaya Medical Centre were obtained, while 12 angular measurements were attained using the Mimics software. The angular data were then divided into 2 groups (patients aged 0 to 24 months and >24 months). This work proposes a 95% confidence interval (CI) for angular mean to detect the abnormality in patient's cranial growth for the SC syndromes., Results: The 95% CI of angular mean for the control group was calculated and used as an indicator to confirm the abnormality in patient's cranial growth that is associated with the 3 syndromes. The results showed that there are different cranial angles associated with these 3 syndromes., Conclusions: All cranial angles of the patients with these syndromes lie outside the 95% CI of angular mean of control group, indicating the reliability of the proposed CI in the identification of abnormality in the patient's cranial growth., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In this study, we propose a new method to detect outlying observations in spherical data. The method is based on the k-nearest neighbours distance theory. The proposed method is a good alternative to the existing tests of discordancy for detecting outliers in spherical data. In addition, the new method can be generalized to identify a patch of outliers in the data. We obtain the cut-off points and investigate the performance of the test statistic via simulation. The proposed test performs well in detecting a single and a patch of outliers in spherical data. As an illustration, we apply the method on an eye data set., Competing Interests: The authors have declared that no competing interests exist.

The aim of the study is to compare the reproductive outcomes of different sperm selection techniques: density gradient centrifugation (DGC), testicular sperm (Testi), physiological ICSI (PICSI), and magnetic-activated cell sorting (MACS) in abnormal sperm DNA fragmentation (SDF) ICSI patients. A randomized controlled trial included 302 patients with abnormal SDF undergoing ICSI where they were randomized into 4 groups: a control group of DGC (n= 72), Testi (n=73), PICSI (n=78), and MACS (n=79). Results showed no significant differences in the male age, female age, or SDF between the four groups. Testi group had significantly lower cleavage and blastulation rates compared to PICSI, DGC, or MACS groups (p =0.001). For the high-quality blastocysts, DGC and MACS groups had significantly higher rate than the Testi group (p =0.014). The highest pregnancy rate was scored for the PICSI group (69.6%), while the lowest pregnancy rate was scored for the DGC group (51.4%) with (p =0.025). The PICSI group showed a significantly higher implantation rate compared to the other groups (p =0.003). Regarding the ongoing pregnancy rate, the significant difference was observed between the PICSI (62.8%) and MACS (62%) vs. DGC (45.8%). Besides, no significant differences were found in the miscarriage rates between the four groups. In conclusion, PICSI and MACS along with DGC showed significant improvement in embryological and clinical outcome over testicular sperm or sperm processed by DGC alone in patients with abnormal SDFRegistration number: NCT04482517., (© 2021. Society for Reproductive Investigation.)

Paroxonase 1 (PON 1) enzymatic activity and Q192R PON polymorphism has been implicated with greater cardiovascular risk in general population. Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized with increased inflammatory markers leading to increased cardiovascular morbidity. The aim of the work was to study association between PON1 enzymatic activity & gene polymorphism with carotid plaques in RA patients. This case-control study was carried out at Zagazig University Hospitals on 99 subjects divided randomly into two groups; 48 RA patients and 51 controls. RA patients fulfilled the revised 2010 EULAR/ACR classification criteria of RA. All patients were subjected to history taking, clinical evaluation, laboratory investigations & plain X-rays. Carotid intima-media thickness (CIMT) and PON1 enzyme assay & genotyping were done for both groups. PON1 enzyme levels were significantly higher in patients than controls. Also, there was a significant negative correlation of PON1 enzyme activity with increased CIMT & plaques. The cut-off value of PON1 enzyme level that had the highest CVD prediction was 4.2 U/ml. Although PON1 genotyping was insignificantly different between patients and controls, patients with QQ genotype had the lowest PON1 activity then patients with QR genotype then RR genotype. In RA patients, decreased serum PON1 enzymatic activity and QQ genotyping of Q192R PON polymorphism was associated with increased CIMT & plaques. Serum PON1 could be a good marker for atherosclerosis prediction in RA patients at cutoff 4.2 U/ml.

A number of circular regression models have been proposed in the literature. In recent years, there is a strong interest shown on the subject of outlier detection in circular regression. An outlier detection procedure can be developed by defining a new statistic in terms of the circular residuals. In this paper, we propose a new measure which transforms the circular residuals into linear measures using a trigonometric function. We then employ the row deletion approach to identify observations that affect the measure the most, a candidate of outlier. The corresponding cut-off points and the performance of the detection procedure when applied on Down and Mardia's model are studied via simulations. For illustration, we apply the procedure on circadian data.