Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included a total of 121 newborns, with 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Continuous monitoring of biosignals, including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST), was conducted. An algorithm was then developed in Python to identify early signs of sepsis. The model demonstrated the capability to detect sepsis 6 to 48 h in advance with an accuracy rate of 87.67 ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. These findings highlight the critical role of non-invasive biosensing methods in conjunction with conventional biomarkers and cultures, offering a strong foundation for early sepsis detection and improved neonatal care. Further research should be conducted to validate these results across different clinical settings.

Newborn monitoring in neonatal intensive care units (NICU) is mandatory, but neurological and especially electroencephalographic (EEG) monitoring can be overlooked or delayed until the newborn is clinically stable. However, the neonatal period is associated with the highest risk of seizures in humans, and the clinical symptoms may often be discrete, but the evolution and long-term neurodevelopmental disorders in these patients may be important. In response to this issue, we conducted a study to evaluate newborns who experienced neonatal seizures (NS) in the NICU and monitored their long-term neurological development. We enrolled 73 term and preterm newborns who underwent EEG monitoring using amplitude-integrated electroencephalography (aEEG). We then followed their neurological development until around 18 months of age, with 59 patients remaining in the long-term study. A total of 22% of patients with NS developed epilepsy, 12% cerebral palsy, 19% severe neurodevelopmental disabilities, and 8.5% died within the first 18 months of life. Our findings indicate that aEEG background pattern is a strong predictor of unfavorable neurological outcomes, with an odds ratio of 20.4174 (p < 0.05). Additionally, higher Apgar scores were associated with better outcomes (p < 0.05), with the odds of unfavorable neurological outcomes decreasing by 0.7-fold for every point increase in Apgar score. Furthermore, we found a statistically significant association between preterm birth and unfavorable neurological outcomes (p = 0.0104). Our study highlights the importance of early EEG monitoring in the NICU and provides valuable insights into predictors of unfavorable neurological outcomes in newborns who experienced NS.

Sepsis still remains one of the most difficult conditions to diagnose as it presents unspecific clinical signs and symptoms, which makes early detection crucial. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with traditional diagnostic methods. The research involved 121 newborns, comprising 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Biosignals including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST) were continuously monitored and an algorithm was developed using Python for identifying early signs of sepsis. The model demonstrated the ability to detect sepsis 6 to 48 hours in advance with an accuracy rate of 87.67% ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. The findings highlight the critical role of non-invasive biosensing methods in conjunction with traditional biomarkers and cultures, offering a robust framework for early sepsis detection and improved neonatal care. Further studies are recommended to validate these results across diverse clinical settings.

Background/Objectives: Perinatal infections caused by Gram-negative bacteria pose a significant risk to neonatal health, especially in low-resource settings. These infections often lead to severe complications due to diagnostic and therapeutic challenges. This study aimed to clinical characterize, early outcomes, and risk factors associated with Gram-negative infections in neonates admitted to the tertiary neonatal intensive care unit (NICU) at “Louis Turcanu” Children’s Hospital, Timisoara. Methods: A retrospective, case–control study was conducted at a tertiary neonatal care unit in Timișoara, Romania, including neonates with microbiologically confirmed Gram-negative infections (n = 44) and a matched control group without infection (n = 47). Clinical, laboratory, and microbiological data were analyzed. Statistical comparisons and logistic regression analyses were used to evaluate risk factors and outcomes. Results: Male sex (79.5% in infected vs. 57.4% in controls; p = 0.0418) and vaginal delivery (43.2% vs. 17.0%; p = 0.00001) were significantly associated with infection. Respiratory distress (72.7%) and digestive symptoms (75.0%) were common in infected neonates. C-reactive protein and procalcitonin levels were markedly elevated in infected infants. Escherichia coli was the most common pathogen, with multidrug-resistant strains observed in bloodstream infections. Mechanical ventilation was required in 75% of infected neonates compared with 16.2% in controls (p < 0.0001). Mortality was higher among infected neonates (25% vs. 4.3% in the control group), although not statistically significant. Conclusions: Gram-negative perinatal infections are associated with considerable morbidity, particularly in male neonates and those delivered vaginally. Early identification, antimicrobial stewardship, and intensive respiratory support are essential to improving outcomes in this vulnerable population.

Background/Objectives: Advancements in personalized medicine have revolutionized drug delivery, enabling tailored treatments based on genetic and molecular profiles. Non-viral vectors, such as polyurethane (PU)-based systems, offer promising alternatives for gene therapy. This study develops mathematical models to analyze PU degradation, DNA/RNA release kinetics, and cellular interactions, optimizing their application in personalized therapy. Methods: This theoretical study utilized mathematical modeling and numerical simulations to analyze PU-based gene delivery, focusing on diffusion, degradation, and cellular uptake. Implemented in Python 3.9, it employed differential equation solvers and adsorption/internalization models to predict vector behavior and optimize delivery efficiency. Results: This study demonstrated that PU degrades in biological environments following first-order kinetics, ensuring a controlled and predictable release of genetic material. The Higuchi diffusion model confirmed a gradual, sustained DNA/RNA release, essential for efficient gene delivery. Simulations of PU adsorption onto cellular membranes using the Langmuir model showed saturation-dependent binding, while the endocytosis model revealed a balance between uptake and degradation. These findings highlight PU’s potential as a versatile gene delivery vector, offering controlled biodegradability, optimized release profiles, and effective cellular interaction. Conclusions: Our results confirm that PU-based vectors enable controlled biodegradability, sustained DNA/RNA release, and efficient cellular uptake. Mathematical modeling provides a framework for improving PU’s properties, enhancing transport efficiency and therapeutic potential in personalized medicine and gene therapy applications.

Background and Objectives: The administration of iron to premature newborns is a common intervention aimed at preventing iron deficiency (ID). However, there is no consensus on the optimal timing and dosage for iron supplementation in this population. This study evaluates the effects and potential adverse outcomes of administering iron on the 7th and 21st days of life in premature infants. Materials and Methods: This research was conducted on 108 premature neonates at the “Louis Turcanu” Children’s Emergency Clinical Hospital in Timisoara, Romania. The study population was divided into a control group of 48 newborns who did not receive iron supplementation and an intervention group of 60 newborns who did. The analysis utilized univariate and multivariate regression to examine binary outcomes. Results: The findings indicate that iron supplementation significantly increased the risk of anemia during the premature period at 21 days of life, as demonstrated by both univariate and multivariate regression analyses, with an odds ratio (OR) of 2.40 (95% CI, 1.01–5.68) and an adjusted odds ratio (AOR) of 2.75 (95% CI, 1.06–7.11), respectively. Contrary to expectations, iron supplementation did not significantly alter the risk of abnormal serum ferritin or iron levels at 21 days of life, according to the univariate analysis (p = 0.380 and p = 0.526, respectively). Conclusions: The observed increase in the risk of anemia without a corresponding improvement in the serum ferritin or iron levels suggests the need for further investigation into alternative strategies for iron supplementation in premature newborns.