Placental Exosomes as Biomarkers for Maternal Diseases: Current Advances in Isolation, Characterization, and Detection

Biomarkers and diagnostic significance of non-coding RNAs in extracellular vesicles of pathologic pregnancy

Intercellular communication is an important mechanism for the development and maintenance of normal biological processes in all organs, including the female reproductive system. Extracellular vesicles, as important carriers of intercellular communication, contain a variety of biologically active molecules, such as mRNAs, miRNAs, lncRNAs, and circRNAs, which are involved in cell-to-cell exchanges as well as in many physiological and pathological processes in the body. Compared with biomarkers found in tissues or body fluids, extracellular vesicles show better stability due to the presence of their envelope membrane which prevents the degradation of the RNA message in their vesicles. Therefore, the genomic and proteomic information contained in extracellular vesicles can serve as important markers and potential therapeutic targets for female reproductive system-related diseases or placental function. Moreover, changes in the expression of non-coding RNAs (mainly miRNAs, lncRNAs, and circRNAs) in maternal extracellular vesicles can accurately and promptly reflect the progress of female reproductive system diseases. The aim of this review is to collect information on different types of non-coding RNAs with key molecular carriers in female pathologic pregnancies (preeclampsia and recurrent spontaneous abortion), so as to explore the relevant molecular mechanisms in female pathologic pregnancies and provide a theoretical basis for clinical research on the pathogenesis and therapeutic approaches of reproductive system diseases. The current state of the art of exosome isolation and extraction is also summarized.

Shedding Light on Cellular Secrets: A Review of Advanced Optical Biosensing Techniques for Detecting Extracellular Vesicles with a Special Focus on Cancer Diagnosis

In the relentless pursuit of innovative diagnostic tools for cancer, this review illuminates the cutting-edge realm of extracellular vesicles (EVs) and their biomolecular cargo detection through advanced optical biosensing techniques with a primary emphasis on their significance in cancer diagnosis. From the sophisticated domain of nanomaterials to the precision of surface plasmon resonance, we herein examine the diverse universe of optical biosensors, emphasizing their specified applications in cancer diagnosis. Exploring and understanding the details of EVs, we present innovative applications of enhancing and blending signals, going beyond the limits to sharpen our ability to sense and distinguish with greater sensitivity and specificity. Our special focus on cancer diagnosis underscores the transformative potential of optical biosensors in early detection and personalized medicine. This review aims to help guide researchers, clinicians, and enthusiasts into the captivating domain where light meets cellular secrets, creating innovative opportunities in cancer diagnostics.

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.

Thermosensitive Capturer Coupled with the CD63 Aptamer for Highly Efficient Isolation of Exosomes

Exosomes are bioactive substances secreted by various cells that play a crucial role in cell communication. Due to their nanoscale size and interference from nonexosome proteins, the rapid capture and nondestructive release of exosomes remain a technical challenge which significantly hinders their biomedical application. To overcome this obstacle, we have designed a CD63 aptamer-based thermosensitive copolymer for the effective isolation of exosomes from mesenchymal stem cells (MSCs). A thermal-responsive copolymer, poly(N-isopropylacrylamide-co-butyl methacrylate-co-N-hydroxysuccinimide methacrylate) P(NIPAM-co-BMA-co-NHSMA, PNB), was prepared, which could realize reversible hydrophilic/hydrophobic phase transition by varying temperature. Then, CD63 aptamers were further modified to the copolymer to form the PNB-aptamer, where the aptamer units, acting as a "lock and key", specifically bind exosomes. Under the low critical solution temperature (LCST) of the PNB-aptamer, it can maintain a hydrophilic state, capturing exosomes from the cell culture medium. Subsequently, exosome-carrying PNB-aptamers can endure from hydrophilic to hydrophobic phase transition by increasing the temperature above its LCST, and then they can be collected after centrifugation. By introducing the complementary sequence of the CD63 aptamer, the stronger binding affinity between the complementary sequence and the aptamers facilitates the release of exosomes from the PNB-aptamer. The yield of exosome samples captured from a MSC culture medium by the PNB-aptamer system (around 62%) is considerably higher than that obtained by the current "gold standard" ultrafiltration (UC) approach (around 42%). Thus, the PNB-aptamer capturer provides a potential strategy for highly efficient exosome isolation.