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    Is the Concentration of Cadmium, Lead, Mercury, and Selenium Related to Preterm Birth?
    (Humana Press Inc, 2019) Yildirim, Engin; Derici, Mehmet Kuersat; Demir, Emre; Apaydin, Hakan; Kocak, Ozguer; Kan, Ozguer; Gorkem, Umit
    Environmental pollution and exposure of people to heavy metals cause many bad obstetric outcomes. Our aim is to demonstrate the role of cadmium (Cd), lead (Pb), mercury (Hg), and selenium (Se) in preterm labor etiology with a case-control study. In this study, between November 2017 and April 2018, preterm delivery mothers and term delivery mothers were compared in corum, Turkey. All deliveries were performed with cesarean sections and there were 30 mothers in the control group and 20 in the study group. The maternal blood, maternal urine, umbilical cord blood, and heavy metal levels in the amnion fluid in both groups were studied. Graphite furnace atomic absorption spectrometry was used to determine the blood concentration of Cd, Pb, Hg, and Se. We found lower levels of selenium in blood and urine of preterm delivery mothers and umbilical cord and amnion fluids of preterm infants (p < 0.01). We found a statistically significant positive correlation at selenium levels between mother's blood and umbilical cord blood (r (50) = 0.896, p < 0.001) and between maternal urine and amniotic fluid (r (50) = 0.841, p < 0.001). We have not found a similar correlation between mother and fetus of other metals (p > 0.05). We found that selenium levels were lower in mothers who were preterm birth in the light of the data in our study. We could not determine the positive or negative correlation of Cd, Pb, and Hg levels in blood, urine, and amniotic fluid samples with preterm birth.
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    Is there a relationship between serum kisspeptin levels and endometrial polyps in women with premenopausal status
    (2019) Yıldırım, Engin; Derici, Mehmet Kürşat; Simsek, Omer Yavuz; Demir, Emre
    Aim: Endometrial polyps are frequently associated with abnormal uterine bleeding. The kispeptin family is one of the peptidesthat play a role in reproductive functions and whose expression varies in various uterine pathologies. The aim of the study was todetermine the relationship between serum Kisspeptin levels and endometrial polyps in women with premenopausal status.Material and Methods: The blood was collected prior to endometrial sampling from women admitted to the hospital due to abnormaluterine bleeding. According to the pathology results, patients were identified as polyp group (n38) (endometrial polyps) and controlgroup (n50) (normal endometrial findings). Kisspeptin-54 levels were determined by ELISA method from serum obtained fromvenous blood.Results: There were no difference was found between the patients’ age, body mass index, gravida, para, abortus and the numberof living children were compared (p0.05). There was no statistically significant difference between the groups in terms of folliclestimulating hormone, luteinizing hormone and thyroid stimulating hormone values (p0.05). Plasma kisspeptin (1.840.93 ng/dLand 1.320.47 ng/dL, p 0.008) and estradiol (90.3413.02 pg/mL and 81.7512.36 pg/mL, p0.002) levels were significantly higherin the polyp group than in the control group. After the Receiver Operating Characteristic (ROC) analysis the area under the curve(AUC) was 1.26 (p 0.08), (95% CI, 0.550-0.782). The sensitivity value was 0.684 (0.512-0.819), the specificity was 0.620 (0.471-0.750).Conclusion: The serum Kisspeptin-54 and estradiol levels were found higher in patients with endometrial polyps.
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    The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis
    (Hindawi Ltd, 2020) Akmese, Omer F.; Dogan, Gul; Kor, Hakan; Erbay, Hasan; Demir, Emre
    Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.