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Publication Metadata only A case of secondary hemophagocytic lymphohistiocytosis (HLH) following incomplete kawasaki's disease (KD). Importance of distinguishing recurrent kd from HLH(2014) Kebudi, R.; Dindar, A.; Gürakan, F.; Devecioğlu, O.; Sözmen, Banu Oflaz; Faculty Member; School of Medicine; 198711N/APublication Open Access A clinical scoring system to predict the development of bronchopulmonary dysplasia(Thieme Medical Publishers, 2015) Hayran, Mutlu; Derin, Hatice; Ovalı, Fahri; N/A; Gürsoy, Tuğba; Faculty Member; School of Medicine; 214691Objective: This study aims to develop a scoring system for the prediction of bronchopulmonary dysplasia (BPD). Methods: Medical records of 652 infants whose gestational age and birth weight were below 32 weeks and 1,500g, respectively, and who survived beyond 28th postnatal day were reviewed retrospectively. Logistic regression methods were used to determine the clinical and demographic risk factors within the first 72 hours of life associated with BPD, as well as the weights of these factors on developing BPD. Predictive accuracy of the scoring system was tested prospectively at the same unit. ResultsBirth weight, gestational age, gender, presence of respiratory distress syndrome, patent ductus arteriosus, intraventricular hemorrhage, hypotension were the most important risk factors for BPD. Therefore, a scoring system (BPD-TM score) ranging from 0 to 13 and grouped in four tiers (0-3: low, 4-6: low intermediate, 7-9: high intermediate, and 10-13: high risk) was developed based on these factors. Below the score of 4, 4.1% of infants (18/436), above the score of 9, 100% (29/29) of the infants developed BPD. The score was validated successfully in 172 infants. Conclusion: With this easy to use scoring system, one can predict the neonate at risk for BPD at 72 hours of life and direct preventive measures toward these infants.Publication Metadata only A deep breath for the mothers of dissabled children(Springer, 2016) N/A; N/A; Ocakçı, Ayşe Ferda; Faculty Member; School of Nursing; 1729N/APublication Metadata only A meta-analysis of anxiety disorder comorbidity in pediatric bipolar disorder(Elsevier Science Inc, 2016) Taşkıran, Ali Sarper; Eser, Hale Yapıcı; Mutluer, Tuba; Kılıç, Özge; Özcan, Aslıhan; Necef, Işıl; Yalçınay-İnan, Merve; Öngür, Dost; Other; Faculty Member; Faculty Member; Doctor; Other; Doctor; Doctor; N/A; School of Medicine; School of Medicine; School of Medicine; N/A; N/A; School of Medicine; N/A; N/A College of Engineering; School of Medicine; School of Medicine; N/A; N/A; School of Medicine; N/A; N/A; Koç University Hospital; 195168; 134359; 305311; N/A; N/A; N/A; N/A; N/AObjectives: AD are among the most prevalent comorbid conditions in pediatric bipolar disorder (PBD). There are conflicting results in the literature regarding prevalence of AD subtypes, and significant discrepancy with PBD course of illness (episodic or chronic) or diagnostic criteria (narrow or broad). Our aim in conducting meta-analysis is to investigate the prevalence of subtypes of comorbid anxiety disorders and its relations with the onset (childhood or adolescent) and course (episodic or chronic) of PBD. Methods: We have conducted a systematic research of Pubmed by using “bipolar disorder, affective psychosis, generalized anxiety disorder, panic, social phobia, obsessive compulsive disorder and anxiety disorder” as keywords to search in title/abstract until September 2015. Among 3202 articles, a total of 430 abstracts were found to be related; 82 were conducted in pediatric population, which were read in full text by at least two authors and data was extracted for outcome measures. Articles that include the data from the same population sample were excluded. Data was analyzed with random effects model using R statistical program package. Results: Data from 33 studies were included in the final analysis. The prevalence of any AD in PBD was 44 percent (95% CI 0.38–0.50), prevalence of AD subtypes were GAD 25 percent (95% CI 0.18–0.36); Separation Anxiety Disorder (SAD) 22 percent (95% CI 0.14–0.33); OCD 17 percent (95% CI 0.11–0.23); Social Phobia (SP) 15 percent (95% CI 0.08–0.27); Panic Disorder (PD) 10 percent (95% CI 0.05–0.19). When only episodic PBD were concerned, prevalence rates differed, with any AD 38 percent (95% CI 0.28–0.48); GAD 19 percent (95% CI 0.08–0.41); SAD 21 percent (95% CI 0.10–0.40); OCD 11 percent (95% CI 0.03–0.29); SP 11 percent (95% CI 0.04–0.27); PD 9 percent (95% CI 0.03–0.23). Prevalence of any AD (34% (95% CI 0.23-0.48), GAD and SAD were found as lower and OCD, SP and PD were slightly higher in adolescent onset compared to childhood onset PBD. Conclusions: Youth with BD are at increased risk of AD; nearly one in two has an AD. GAD and SAD are among the most prevalent comorbidities. AD are seen less with episodic and adolescent onset PBD. AD should be carefully investigated alongside the mood symptoms in PBD, as comorbidity may change course, treatment and subtyping of the disorder.Publication Open Access A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children(Springer, 2020) Türkmen, İnan Utku; Namlı, Gözde; Özturk, Çiğdem; Esen, Ayşe B.; Eray, Y. Nur; Akova, Fatih; Aydın, Emrah; Eroğlu, Egemen; Faculty Member; faculty Member; School of Medicine; 32059; N/AIntroduction: there is a tendency toward nonoperative management of appendicitis resulting in an increasing need for preoperative diagnosis and classification. For medical purposes, simple conceptual decision-making models that can learn are widely used. Decision trees are reliable and effective techniques which provide high classification accuracy. We tested if we could detect appendicitis and differentiate uncomplicated from complicated cases using machine learning algorithms. Materials and methods: we analyzed all cases admitted between 2010 and 2016 that fell into the following categories: healthy controls (Group 1); sham controls (Group 2); sham disease (Group 3), and acute abdomen (Group 4). The latter group was further divided into four groups: false laparotomy; uncomplicated appendicitis; complicated appendicitis without abscess, and complicated appendicitis with abscess. Patients with comorbidities and whose complete blood count and/or pathology results were lacking were excluded. Data were collected for demographics, preoperative blood analysis, and postoperative diagnosis. Various machine learning algorithms were applied to detect appendicitis patients. Results: there were 7244 patients with a mean age of 6.84 +/- 5.31 years, of whom 82.3% (5960/7244) were male. Most algorithms tested, especially linear methods, provided similar performance measures. We preferred the decision tree model due to its easier interpretability. With this algorithm, we detected appendicitis patients with 93.97% area under the curve (AUC), 94.69% accuracy, 93.55% sensitivity, and 96.55% specificity, and uncomplicated appendicitis with 79.47% AUC, 70.83% accuracy, 66.81% sensitivity, and 81.88% specificity. Conclusions: machine learning is a novel approach to prevent unnecessary operations and decrease the burden of appendicitis both for patients and health systems.Publication Open Access A rare case of juvenile amyotrophic lateral sclerosis(Turkish National Pediatric Society, 2021) Bodur, Muhittin; Toker, Rabia Tütüncü; Okan, Mehmet Sait; Başak, Ayşe Nazlı; Faculty Member; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); School of Medicine; 1512Background: amyotrophic lateral sclerosis (ALS) is a chronic motor neuron disease characterised by progressive weakness in striated muscles resulting from the destruction of neuronal cells. The term juvenile ALS (JALS) is used for patients whose symptoms start before 25 years of age. JALS may be sporadic or familial. Case: here, we present a sporadic case of JALS because of its rarity in children. The heterozygous p.Pro525Leu (c.1574C>T) variation was identified in the fused in sarcoma (FUS) gene. Conclusion: the p.Pro525Leu mutation in the FUS gene has been detected in patients with ALS, characterised by early onset and a severely progressive course.Publication Metadata only A rare cause of AA amyloidosis and end-stage kidney failure: questions and answers(Springer, 2019) Baba, Zeliha Füsun; Taşdemir, Mehmet; Yılmaz, Sezen Güçlü; Bilge, İlmay; Faculty Member; Undergraduate Student; Faculty Member; School of Medicine; School of Medicine; School of Medicine; 175867; N/A; 198907N/APublication Metadata only A rare cause of chronic hyponatremia in an infant: aldosterone synthase type-2 deficiency(Springer, 2018) Güran, Tülay; Yeşiltepe Mutlu, Rahime Gül; Taşdemir, Mehmet; Kızılkan, Nuray Uslu; Börklü Yücel, Esra; Hatun, Şükrü; Kayserili, Hülya; Bilge, İlmay; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; 153511; N/A; 221274; N/A; 153504; 7945; 198907N/APublication Metadata only A rare cause of chronic hyponatremia in an infant: answers(Springer, 2020) Güran, T.; N/A; Yeşiltepe Mutlu, Rahime Gül; Taşdemir, Mehmet; Kızılkan, Nuray Uslu; Hatun, Şükrü; Kayserili, Hülya; Bilge, İlmay; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; 153511; 175867; 221274; 153504; 7945; 198907N/APublication Metadata only A rare cause of chronic hyponatremia in an infant: questions(Springer, 2020) Guran, Tulay; N/A; Yeşiltepe Mutlu, Rahime Gül; Taşdemir, Mehmet; Kızılkan, Nuray Uslu; Hatun, Şükrü; Kayserili, Hülya; Bilge, İlmay; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; 153511; 175867; 221274; 153504; 7945; 198907N/A