Silver nanoparticles have been intensively studied over a long period of time because they exhibit antibacterial properties in infection treatments, wound healing, or drug delivery systems. The advantages that silver nanoparticles offer regarding the functionalization confer prolonged stability and make them suitable for biomedical applications. Apart from functionalization, silver nanoparticles exhibit various shapes and sizes depending on the conditions used through their fabrications and depending on their final purpose. This paper presents a review of silver nanoparticles with respect to synthesis procedures, including the polluting green synthesis. Currently, the most commonly used characterization techniques required for nanoparticles investigation in antibacterial treatments are described briefly, since silver nanoparticles possess differences in their structure or morphology.

The size of silver nanoparticles plays a crucial role in their ultimate application in the medical and industrial fields, as their efficacy is enhanced by decreasing dimensions. This study presents two chemical synthesis procedures for obtaining silver particles and compares the results to a commercially available Ag-based product. The first procedure involves laboratory-based chemical reduction using D-glucose (C₆H₁₂O₆) and NaOH as reducing agents, while the second approach utilizes trisodium citrate dehydrate (C₆H₅Na₃O₇·2H₂O, TSC). The Ag nanoparticle suspensions were examined using FT-IR and UV-VIS spectroscopy, which indicated the formation of Ag particles. The dimensional properties were investigated using Atomic Force Microscopy (AFM) and confirmed by Dynamic Light Scattering (DLS). The results showed particle size from microparticles to nanoparticles, with a particle size of approximately 60 nm observed for the laboratory-based TSC synthesis approach.

Nanocomposites based on silver nanoparticles and chitosan present important advantages for medical applications, showing over time their role in antibacterial evaluation. This work presents the comparative study of two chemical synthesis procedures of nanocomposites, based on trisodium citrate dihydrate and sodium hydroxide, using various chitosan concentrations for a complex investigation. The nanocomposites were characterized by AFM and DLS regarding their dimensions, while FT-IR and UV–VIS spectrometry were used for the optical properties and to reveal the binding of silver nanoparticles with chitosan. Their antibacterial effect was determined using a disk diffusion method on two bacteria strains, E. coli and S. aureus. The results indicate that, when using both methods, the nanocomposites obtained were below 100 nm, yet the antibacterial effect proved to be stronger for the nanocomposites obtained using sodium hydroxide. Furthermore, the antibacterial effect can be related to the nanocomposites’ sizes, since the smallest dimension nanocomposites exhibited the best bacterial growth inhibition on both bacteria strains we tested and for both types of silver nanocomposites.

Dynamic Light Scattering is a technique currently used to assess the particle size and size distribution by processing the scattered light intensity. Typically, the particles to be investigated are suspended in a liquid solvent. An analysis of the particular conditions required to perform a light scattering experiment on particles in air is presented in detail, together with a simple experimental setup and the data processing procedure. The results reveal that such an experiment is possible and using the setup and the procedure, both simplified to extreme, enables the design of an advanced sensor for particles and fumes that can output the average size of the particles in air.

Ovarian follicular fluid (FF) is a metabolically active and biomarker-rich medium that mirrors the oocyte microenvironment. Its analysis is increasingly recognized in infertility diagnostics and assisted reproductive technologies (ART) for assessing oocyte competence, understanding reproductive disorders, and guiding personalized treatment. However, FF’s high viscosity, complex composition, and biochemical variability challenge reproducibility in sample preparation and molecular profiling. Sonication-based extraction has emerged as an effective approach to address these issues. By exploiting acoustic cavitation, sonication improves protein solubilization, metabolite release, and lipid recovery, while reducing solvent use and processing time. This review synthesizes recent advances in sonication-assisted FF analysis across proteomics, metabolomics, and lipidomics, emphasizing parameter optimization, integration with advanced mass spectrometry workflows, and emerging applications in microfluidics, automation, and point-of-care devices. Clinical implications are discussed in the context of enhanced biomarker discovery pipelines, real-time oocyte selection, and ART outcome prediction. Key challenges, such as preventing biomolecule degradation, standardizing protocols, and achieving inter-laboratory reproducibility, are addressed alongside regulatory considerations. Future directions highlight the potential of combining sonication with multi-omics strategies and AI-driven analytics, paving the way for high-throughput, standardized, and clinically actionable FF analysis to advance precision reproductive medicine.

Background: As the COVID-19 pandemic evolved, concerns grew about its impact on pregnant women. This study aimed to determine how SARS-CoV-2 affects pregnancy, birth, and newborns, in order to identify vulnerable individuals and provide proper care. Methods: This is a retrospective case-control study of 398 pregnant women who delivered at the Emergency Clinical County Hospital in Sibiu, Romania from 1 February 2020 to 31 March 2022. Patients were initially grouped and compared based on their RT-PCR SARS-CoV-2 test results into the COVID group (cases) (N = 199) and non-COVID group (control) (N = 199). The COVID cases were further divided and compared according to the pre-Delta (N = 105) and Delta/Omicron (N = 94) SARS-CoV-2 variants. COVID cases and control groups were compared to identify correlations between sociodemographic factors, pregnancy outcomes, and SARS-CoV-2 infection. The same comparisons were performed between pre-Delta and Delta/Omicron groups. Results: There were no significant differences concerning maternal residence, while educational level and employment proportion were higher among the positively tested patients. No significant differences were found for neonatal and pregnancy complications between COVID cases and control groups. Except for a lower mean gestational age, no significant differences were found between pre-Delta and Delta/Omicron periods. The maternal mortality in the infected group was 0.5% (1 case). Conclusions: Our study showed that SARS-CoV-2 infection at birth did not significantly affect maternal and neonatal outcomes, not even considering the SARS-CoV-2 strain.