(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. The patients’ clinical characteristics were extracted from a structured survey and were included in four machine learning-based models: support vector machine (SVM), random forest (RF), naïve Bayes (NB), and K nearest neighbors (KNN), and their predictive performance was assessed. (3) Results: All evaluated models performed better when used to predict HCV status. The highest predictive performance was achieved by KNN algorithm (accuracy: 98.1%), followed by SVM and RF with equal accuracies (97.6%) and NB (95.7%). The predictive performance of these models was modest for HBV status, with accuracies ranging from 78.2% to 97.6%. (4) Conclusions: The machine learning-based models could be useful tools for HCV infection prediction and for the risk stratification process of adult patients who undergo a viral hepatitis screening program.

(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, n = 59 patients), and Group 2 (without ROP, n = 90 patients). The patients that were affected by ROP were subsequently divided into the following subgroups: Subgroup 1 (Stage 1, n = 21), Subgroup 2 (Stage 2, n = 35), and Subgroup 3 (Stage 3, n = 25). The associations were analyzed using multivariate logistic regression and sensitivity analysis. (3) Results: Platelet mass indexes (PMI) that were determined in the first, seventh, and tenth days of life were significantly associated with Stage 1 ROP. PMI determined in the first day of life was also significantly associated with Stage 2 ROP. The sensitivity and specificity of these parameters were modest, ranging from 44 to 57%, and 59 to 63%. (4) Conclusions: PMI has a modest ability to predict the development of ROP.

Background and Objectives: Preterm birth (PTB) is associated with important neonatal mortality and morbidity. The aim of this study was to retrospectively evaluate the average treatment effects on the treated and the efficacity of various therapeutic interventions for PTB in a cohort of patients with singleton pregnancies and short cervical lengths. Materials and Methods: This observational retrospective study included 1146 singleton pregnancies at risk of PTB that were segregated into the following groups: intravaginal progesterone (group 1), Arabin pessary (group 2), McDonald cerclage (group 3), intravaginal progesterone and Arabin pessary (group 4), and intravaginal progesterone and cerclage (group 5). Their treatment effects were evaluated and compared. Results: All evaluated therapeutic interventions significantly reduced the occurrence of late and early preterm births. The risk of late and early PTB was lowered for those pregnant patients who received progesterone and pessaries or progesterone and cerclage in comparison with those who received only progesterone. The extremely PTB risk of occurrence was significantly lowered only by the administration of progesterone in association with cervical cerclage in comparison with progesterone monotherapy. Conclusions: The combined therapeutic interventions had the highest efficacy in preventing preterm birth. An individualized evaluation is needed to establish the best therapeutic approach in particular cases.