Chitosan (CS) occurs naturally as an alkaline polysaccharide and has been demonstrated to have several activities of a biological nature. Additionally, as CS chains have functional hydroxyl and amino groups that are active, their applications can be expanded by chemically or molecularly altering the molecules to incorporate new functional groups. Due to its outstanding qualities, including biodegradability, biocompatibility, non-toxicity, and accessibility, it has received significant interest in all areas of biomedicine and nanomaterials being extremely promising as drug nanocarrier. The last decades have produced a lot of interest in CS-based nanoparticles (CSNPs), with an increasing number of research papers from around 1500 in 2015 to almost 5000 in 2024. The degree of crosslinking, the particulate system’s shape, size, and density, in addition to the drug’s physical and chemical properties, all have a role in how the drug is transported and released from CSNPs. When creating potential drug delivery systems based on CSNPs, all these factors must be considered. In earlier, CSNPs were employed to enhance the pharmacotherapeutics, pharmacokinetics, and solubility properties of drugs. By investigating its positively charged characteristics and changeable functional groups, CS has evolved into a versatile drug delivery system. The drug release from CSNPs will definitely be influenced by various changes to the functional groups, charges, and polymer backbone. This review mainly discusses the most important results published in the last decade. Despite the promising advantages of CSNPs, challenges related to the translation into clinical stages remain and further in vitro and in vivo studies are mandatory.

Background/Objectives: Multidrug-resistant urinary tract infections (MDR UTIs) constitute an important public health problem, especially in pregnant patients. The aim of this retrospective study was to characterize the bacterial spectrum and the profile of microbial resistance in cases of UTIs occurring in pregnant women, as well as their impact on obstetrical and neonatal outcomes. Methods: A total of 371 pregnant patients with UTIs were included in the analysis and were segregated into the following groups based on the type of bacterial resistance to antibiotics: MDR UTIs (70 patients, group 1), UTIs resistant to one class of antibiotics (108 patients, group 2), UTIs resistant to two classes of antibiotics (102 patients, group 3), and sensitive UTIs (91 patients, group 4). We used descriptive statistics for characterizing and comparing the microbial spectrum and the clinical characteristics of the patients. A multinomial logistic regression model for evaluating the relationship between the type of urinary tract infection and adverse obstetric or neonatal outcomes was employed. Results: In the case of MDR UTIs, the bacterial spectrum mainly included Escherichia coli, Enterococcus faecalis, and Klebsiella species. We found almost universal resistance to ampicillin. Our data confirmed an increased risk of preterm birth, premature rupture of membranes, neonatal respiratory distress syndrome, and neonatal intensive care unit admission for patients with MDR infections. Conclusions: The increased incidence of pathogens resistant to commonly used antibiotic classes in pregnancy suggests the need for the development of local and national protocols that adapt therapeutic and prophylactic regimens to clinical realities.

Background and Objectives: Polycystic ovary syndrome (PCOS) is a complex disorder that can negatively impact the obstetrical outcomes. The aim of this study was to determine the predictive performance of four machine learning (ML)-based algorithms for the prediction of adverse pregnancy outcomes in pregnant patients diagnosed with PCOS. Materials and Methods: A total of 174 patients equally divided into 2 groups depending on the PCOS diagnosis were included in this prospective study. We used the Mantel–Haenszel test to evaluate the risk of adverse pregnancy outcomes for the PCOS patients and reported the results as a crude and adjusted odds ratio (OR) with a 95% confidence interval (CI). A generalized linear model was used to identify the predictors of adverse pregnancy outcomes in PCOS patients, quantifying their impact as risk ratios (RR) with 95% CIs. Significant predictors were included in four machine learning-based algorithms and a sensitivity analysis was employed to quantify their performance. Results: Our crude estimates suggested that PCOS patients had a higher risk of developing gestational diabetes and had a higher chance of giving birth prematurely or through cesarean section in comparison to patients without PCOS. When adjusting for confounders, only the odds of delivery via cesarean section remained significantly higher for PCOS patients. Obesity was outlined as a significant predictor for gestational diabetes and fetal macrosomia, while a personal history of diabetes demonstrated a significant impact on the occurrence of all evaluated outcomes. Random forest (RF) performed the best when used to predict the occurrence of gestational diabetes (area under the curve, AUC value: 0.782), fetal macrosomia (AUC value: 0.897), and preterm birth (AUC value: 0.901) in PCOS patients. Conclusions: Complex ML algorithms could be used to predict adverse obstetrical outcomes in PCOS patients, but larger datasets should be analyzed for their validation.

This study describes the comparison between the interaction of a series of peptide-functionalized chitosan-based nanocapsules and liposomes with two cell lines, i.e., mouse macrophages RAW 264.7 and human endothelial cells EA.hy926. Both types of nanocarriers are loaded with magnetic nanoparticles and designed for anti-inflammatory therapy. The choice of these magnetic nanostructures is argued based on their advantages in terms of size, morphology, chemical composition, and the multiple possibilities of modifying their surface. Moreover, active targeting might be ensured by using an external magnetic field. To explore the impact of chitosan-based nanocapsules and liposomes on cell cytophysiology, the cell viability, using the MTT assay, and cell morphology were investigated. The results revealed low to moderate cytotoxicity of free nanocapsules and significant cytotoxicity induced by chitosan-coated liposomes loaded with dexamethasone, confirming its release from the delivery system. Thus, after 48 h of treatment with nanocapsules, the viability of RAW 264.7 cells varied between 88.18% (OCNPM-1I, 3.125 µg/mL) and 76.37% (OCNPM-1, 25 µg/mL). In the same conditions, EA.hy926 cell viability was between 99.91% (OCNPM-3, 3.125 µg/mL) and 75.15% (OCNPM-3, 25 µg/mL) at the highest dose (25 µg/mL), the values being comparable for both cell lines. Referring to the cell reactivity after dexamethasone-loaded liposome application, the lowest viability of RAW 264.7 cells was 41.25% (CLDM5CP-1, 25 µg/mL) and 58.20% (CLDMM2CP-1 1.25 µg/mL) in the endothelial cell line, proving a selective character of action of nanocarriers. The cell morphology test, performed to support and confirm the results obtained by the MTT test, revealed a differentiated response for the two types of nano-carriers. As expected, an intense cytotoxic effect in the case of dexamethasone-loaded liposomes and a lack of cytotoxicity for drug-free nanocapsules were noticed. Therefore, our study demonstrated the biocompatible feature of the studied nanocarriers, which highlights them for future research as potential drug delivery systems for pharmacological applications, including anti-inflammatory therapy.

(1) Background: Trial of labor after cesarean (TOLAC) can be associated with significant maternal and neonatal complications, and the aim of this retrospective study was to calculate the risks and probabilities of these complications in two tertiary maternity centers in Romania. (2) Methods: A total of 216 patients who attempted TOLAC were included in the study and were segregated into two groups, depending on TOLAC success. Medical records were assessed, and clinical data were used to determine the maternal and neonatal risks and complications, using multinomial logistic regression and postestimation predictions. (3) Results: Our data indicated that patients who had a failed TOLAC had significantly higher risks and probabilities of uterine rupture, either complete or incomplete; intensive care unit (ICU) admission; and emergency hysterectomy. The newborns of these mothers had significantly higher risks and probabilities of low Apgar score at birth, neonatal intensive care unit (NICU) admission, and invasive ventilation. (4) Conclusions: Failed TOLAC could lead to significant maternal and neonatal complications, and women who attempt this procedure should be monitored in a tertiary center where a multidisciplinary team and an emergency operating room are available.

Poly(L-lactic acid) (PLLA) and poly(ε-caprolactone) (PCL), two biodegradable and biocompatible polymers that are commonly used for biomedical applications, are, respectively, the result of the ring-opening polymerization of LA and ε-CL, cyclic esters, which can be produced according to several mechanisms (cationic, monomer-activated cationic, anionic, and coordination-insertion), except for L-lactide, which is polymerized only by anionic, cationic, or coordination-insertion polymerization. A series of well-defined PLLA-b-PCL block copolymers have been obtained starting from the same PLLA homopolymer, having a molar mass of 2500 g·mol⁻¹, and being synthesized by coordination-insertion in the presence of tin octoate. PCL blocks were obtained via a cationic-activated monomer mechanism to limit transesterification reactions, and their molar masses varied from 1800 to 18,500 g·mol⁻¹. The physicochemical properties of the copolymers were determined by ¹H NMR, SEC, and DSC. Moreover, a series of nanoparticles (NPs) were prepared starting from these polyester-based copolymers by an emulsification/evaporation method. The sizes of the obtained NPs varied between 140 and 150 nm, as a function of the molar mass of the copolymers. Monomodal distribution curves with PDI values under 0.1 were obtained by Dynamic Light Scattering (DLS) and their spherical shape was confirmed by TEM. The increase in the temperature from 25 to 37 °C induced only a very slight decrease in the NP sizes. The results obtained in this preliminary study indicate that NPs have a temperature stability, allowing us to consider their use as drug-loaded nanocarriers for biomedical applications.