A Prospective Study on the Progression, Recurrence, and Regression of Cervical Lesions: Assessing Various Screening Approaches.

(1) Background: The prediction of cervical lesion evolution is a challenge for clinicians. This prospective study aimed to determine and compare the predictive accuracy of cytology, HPV genotyping, and p16/Ki67 dual staining alone or in combination with personal risk factors in the prediction of progression, regression, or persistence of cervical lesions in human papillomavirus (HPV)-infected patients; (2) Methods: This prospective study included HPV-positive patients with or without cervical lesions who underwent follow-up in a private clinic. We calculated the predictive performance of individual tests (cervical cytology, HPV genotyping, CINtecPlus results, and clinical risk factors) or their combination in the prediction of cervical lesion progression, regression, and persistence; (3) Results: The highest predictive performance for the progression of cervical lesions was achieved by a model comprising a Pap smear suggestive of high-grade squamous intraepithelial lesion (HSIL), the presence of 16/18 HPV strains, a positive p16/Ki67 dual staining result along with the presence of at least three clinical risk factors, which had a sensitivity (Se) of 74.

Prediction of Intrauterine Growth Restriction and Preeclampsia Using Machine Learning-Based Algorithms: A Prospective Study.

(1) Background: Prenatal care providers face a continuous challenge in screening for intrauterine growth restriction (IUGR) and preeclampsia (PE). In this study, we aimed to assess and compare the predictive accuracy of four machine learning algorithms in predicting the occurrence of PE, IUGR, and their associations in a group of singleton pregnancies; (2) Methods: This observational prospective study included 210 singleton pregnancies that underwent first trimester screenings at our institution. We computed the predictive performance of four machine learning-based methods, namely decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), by incorporating clinical and paraclinical data; (3) Results: The RF algorithm showed superior performance for the prediction of PE (accuracy: 96.

Investigating the Association between Serum and Hematological Biomarkers and Neonatal Sepsis in Newborns with Premature Rupture of Membranes: A Retrospective Study.

(1) Background: Neonatal early-onset sepsis (EOS) is associated with important mortality and morbidity. The aims of this study were to evaluate the association between serum and hematological biomarkers with early onset neonatal sepsis in a cohort of patients with prolonged rupture of membranes (PROM) and to calculate their diagnostic accuracy. (2) Methods: A retrospective cohort study was conducted on 1355 newborns with PROM admitted between January 2017 and March 2020, who were divided into two groups: group A, with PROM ≥ 18 h, and group B, with ROM < 18 h.

Pregnancy Outcomes in Patients with Urosepsis and Uncomplicated Urinary Tract Infections-A Retrospective Study.

: Urinary tract infections (UTIs) are an important cause of perinatal and maternal morbidity and mortality. The aim of this study was to describe and compare the main pregnancy outcomes among pregnant patients with complicated and uncomplicated UTIs; : This retrospective study included 183 pregnant patients who were evaluated for uncomplicated UTIs and urosepsis in the Urology Department of 'C.I.

Managing Fetal Ovarian Cysts: Clinical Experience with a Rare Disorder.

: Fetal ovarian cysts (FOCs) are a very rare pathology that can be associated with maternal-fetal and neonatal complications. The aim of this study was to assess the influence of ultrasound characteristics on FOC evolution and therapeutic management. : We included cases admitted to our perinatal tertiary center between August 2016 and December 2022 with a prenatal or postnatal ultrasound evaluation indicative of FOC.

Nosocomial Infections Affecting Newborns with Abdominal Wall Defects.

Unlabelled: Abdominal wall defects are serious birth defects, with long periods of hospitalization and significant costs to the medical system. Nosocomial infection (NI) may be an additional risk factor that aggravates the evolution of newborns with such malformations.

Methods: in order to analyze the factors that may lead to the occurrence of NI, we performed a retrospective study over a period of thirty-two years (1990-2021), in a tertiary children's hospital; 302 neonates with omphalocele and gastroschisis were eligible for the study.

Pregnancy Outcomes, Immunophenotyping and Immunohistochemical Findings in a Cohort of Pregnant Patients with COVID-19-A Prospective Study.

(1) Background: SARS-CoV-2 infection during pregnancy could determine important maternal and fetal complications. We aimed to prospectively assess placental immunohistochemical changes, immunophenotyping alterations, and pregnancy outcomes in a cohort of patients with COVID-19; (2) Methods: 52 pregnant patients admitted to a tertiary maternity center between October 2020 and November 2021 were segregated into two equal groups, depending on the presence of SARS-CoV-2 infection. Blood samples, fragments of umbilical cord, amniotic membranes, and placental along with clinical data were collected.

Hematologic Risk Factors for the Development of Retinopathy of Prematurity-A Retrospective Study.

(1) Background: Retinopathy of prematurity (ROP) can cause severe visual impairment or even blindness. We aimed to assess the hematological risk factors that are associated with different stages of ROP in a cohort of preterm newborns, and to compare the clinical characteristics and therapeutic interventions between groups. (2) Methods: This retrospective study included 149 preterm newborns from a tertiary maternity hospital in Romania between January 2018 and December 2018, who were segregated into: Group 1 (with ROP, = 59 patients), and Group 2 (without ROP, = 90 patients).

Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity.

(1) Background: The identification of patients at risk for hepatitis B and C viral infection is a challenge for the clinicians and public health specialists. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HBV and HCV status. (2) Methods: This prospective cohort screening study evaluated adults from the North-Eastern and South-Eastern regions of Romania between January 2022 and November 2022 who underwent viral hepatitis screening in their family physician's offices.

Can Bioactive Food Substances Contribute to Cystic Fibrosis-Related Cardiovascular Disease Prevention?

Advances in cystic fibrosis (CF) care have significantly improved the quality of life and life expectancy of patients. Nutritional therapy based on a high-calorie, high-fat diet, antibiotics, as well as new therapies focused on CFTR modulators change the natural course of the disease. They do so by improving pulmonary function and growing BMI.

Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study.

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022.

Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms-Results from a Retrospective Study.

(1) Background: HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome is a rare and life-threatening complication of preeclampsia. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HELLP syndrome, and its subtypes according to the Mississippi classification; (2) Methods: This retrospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between January 2007 and December 2021. The patients' clinical and paraclinical characteristics were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), k-nearest neighbors (KNN), and random forest (RF), and their predictive performance were assessed; (3) Results: Our results showed that HELLP syndrome was best predicted by RF (accuracy: 89.

Ganglioneuroma of the Bladder in Association with Neurofibromatosis Type 1.

Neurofibromatosis type 1 (NF1) is a genetic disease, with autosomal dominant transmission, related to pathogenic variant of the tumor suppressor gene (17q11.2), predisposing affected subjects to a variety of benign (neurofibromas and plexiform neurofibromas) and malignant tumors. The lack of the NF1-neurofibromin gene product can cause uncontrolled cell proliferation in the central or peripheral nervous system and multisystemic involvement, and so the disease includes a heterogeneous group of clinical manifestations.