3D melt electrowritten (MEW) polycaprolactone (mPCL) scaffolds enhance osteoblast-derived sEVs yield and enrich EV proteomes, highlighting proteins linked to cell adhesion, complement, and tight junctions, with potential for bone tissue engineering.

Ovarian cancer is the most common gynaecological malignancy and the seventh most diagnosed cancer in females worldwide. Currently, it is the sixth leading cause of cancer related mortality among patients globally. The heterogenous origin of the disease and unambiguous nature of the clinical symptoms leading to delayed detection has been one of the key reasons for increasing mortality. Hence new approaches are required to understand the biology of ovarian cancer, where the use of cell culture models that mimic the physiology of the disease is fundamental. Cell culture serves as a crucial in vitro tool, contributing to our comprehension of various aspects of cell biology, tissue morphology, disease mechanisms, drug responses, protein production, and tissue engineering. A significant portion of in vitro studies rely on two-dimensional (2D) cell cultures, however, these cultures present notable limitations, for example disruptions in cellular and extracellular interactions, alterations in cell morphology, polarity, and division mechanisms. Recently, extracellular vesicles have been identified as crucial players in cell biology as part of the communication system that cancer cells use to metastasize. We optimized and compared three-dimensional (3D) culture of ovarian cancer cells lines (SKOV-3 and OVCAR-3) with two-dimensional models based on their protein and miRNA content. We further investigated whether extracellular vesicles from these models reflect changes in cancer cells, and aid in the identification of overall survival in women with ovarian cancer.

Extracellular vesicles (EVs) are lipids bilayer-delimited particles carrying bioactive molecules such as proteins, lipids, and nucleic acids, reflecting the physiological state of their origin. Found in biofluids like saliva, urine, blood, and peritoneal fluid, EVs serve as promising minimally invasive biomarkers for several conditions including cancer. However, achieving high sensitivity and specificity in EV detection remains technically challenging. Placental Alkaline Phosphatase (PLAP), an enzyme primarily expressed in the placenta during pregnancy, has emerged as a clinically relevant biomarker in gynecological malignancies, including ovarian cancer (OC). In this study, a nanoengineered mesoporous gold (mAu)-based Surface-Enhanced Raman Spectroscopy (SERS) platform is reported for the rapid and ultrasensitive detection of PLAP-positive EVs in OC patients. The mAu offers high surface roughness, enabling numerous localized plasmonic hotspots that amplify Raman signals and improve probe and antibody loading. This allowed the detection of as few as 100 EVs mL-1 with excellent reproducibility (RSD < 5%,n = 3). In clinical validation (n = 30), the assay achieved 90% sensitivity (95% CI: 60%-100%) and 85% specificity (95% CI: 15%-100%) in distinguishing OC patients from those with benign and healthy controls, demonstrating superior performance compared to CA-125. The mAu-SERS platform shows considerable promise as a minimally invasive and clinically applicable diagnostic strategy for OC, especially for differential diagnosis for their ability to distingluis between benign and OC conditions.

Herein, we developed a specific, rapid sensor to quantify placental extracellular vesicle (EV) protein biomarkers of early pregnancy complications. A distinct tetraspanin CD9 and placental alkaline phosphatase (PLAP) expression pattern was observed via targeted multiple reaction monitoring of EVs from maternal plasma collected before 18 weeks of gestation. A classification model was developed using training and validation patient sets, distinguishing between individuals at high risk of developing complications from those with normal pregnancies, achieving 80% sensitivity, 90% specificity, 89% positive predictive value (PPV), and 82% negative predictive value (NPV). Superparamagnetic nanoflowers that captured target EVs (CD9+/PLAP+) were used to construct a 4-flex glass strip nanozymatic readout system. The sensor analyzes plasma for EVs, identifying gestational diabetes mellitus risk with a 95% combined sensitivity, 100% specificity, 100% PPV, and 96% NPV. This nanoplatform identifies individuals at risk of developing pregnancy complications with a >90% classification accuracy, exhibiting potential for clinical applications.

Traumatic brain injury (TBI) remains a major global health challenge with a need for improved diagnostic and prognostic biomarkers. This study aimed to evaluate the biomarker potential of extracellular vesicle (EV)-encapsulated glial fibrillary acidic protein (EV-GFAP), neurofilament light chain (EV-NfL), total tau (EV-T-Tau), and ubiquitin carboxy-terminal hydrolase L1 (EV-UCH-L1) in TBI. A cohort of 93 trauma patients (75 with TBI and 18 without TBI) was analyzed. Patients were sampled on admission, as well as 15 and 72 h post-injury. Following initial method validation, EVs were isolated from plasma using size exclusion chromatography (SEC), and plasma levels and EV cargo levels of biomarkers were measured using an ultra-sensitive Single Molecule Array. EV-GFAP levels were significantly elevated in TBI patients compared to non-TBI trauma patients at admission and 15 h. A positive head CT was associated with 2.85 (95% CI: 1.18–6.91) fold increased EV-GFAP, whereas EV-NfL, EV-T-Tau, and EV-UCH-L1 levels were not affected. None of the tested EV biomarkers were associated with 1-year mortality or 6–12 months’ functional outcome. Plasma-GFAP levels increased 3.4 (95% CI: 1.72–6.70) fold with a positive head CT but were not associated with outcomes. EV-GFAP shows potential as an early biomarker of TBI, but plasma-GFAP remains a practical and reliable alternative. Future studies should explore the potential complementary roles of EV-based biomarkers on alternative aspects of TBI pathophysiology and prediction of long-term outcomes. Studies should refine methods to enhance reproducibility and clinical applicability.

5582 Background: The high mortality of Ovarian cancer (OC) has been attributed to late-stage diagnosis and the lack of an effective early detection strategy, particularly for asymptomatic women. In this study, we developed and validated a high-throughput OC detection test based on plasma extracellular vesicle (EV)-associated biomarkers. Methods: A case-control study was conducted to evaluate blood-borne EV-associated ovarian cancer biomarkers, including miRNAs, proteins, lncRNAs, miscRNAs, MtrRNAs, MttRNAs, rRNAs, scaRNAs, snRNAs, and tRNAs. Protein and RNA biomarkers were identified by mass spectrometry and RNA sequencing, respectively. Training (n=453) and independent test (n=471) sample sets were used to develop and validate a multivariate index assay (MIA). The MIA was further validated using a high-throughput, pathology laboratory compatible, EV isolation platform (EXO-NET) and two independent sample cohorts (n=97 and n=532). The classification accuracy, sensitivity and specificity of the MIA was compared to that of CA125 levels. Results: Discovery and Training phases - more than 100,000 EV-associated biomarkers were identified from 453 EV samples. The classification performance of these biomarkers was assessed using machine learning algorithms. EV-associated protein and miRNA biomarkers delivered the highest performing classifiers and, therefore, were used in subsequent MIA development and training. During the training phase, multivariate classification algorithms were validated using a 10-fold cross-validation method. The highest performing classifiers for EV-associated protein and miRNA, at specificity of 98%, achieved sensitivities of 90% and 82%, respectively. Validation phase: Locked classification algorithms ( i.e. MIAs) were validated using two independent sample cohorts and reported classification accuracies of 92-98%, significantly outperforming CA-125 (CE = 62%, p<0.001). Automated high-throughput MIA – All stages OC: the best performing automated high-throughput MIA demonstrated an overall sensitivity of 92% (95% CI, 75–96%) and specificity of 93% (95% CI, 86–96%) for all stages of OC, Positive Predictive Value of 95% (CI, 93-96%) and Negative Predictive Value of 80% (CI, 76-89%) at 98% specificity (n=532). Stage I OC: Importantly, the MIA displayed a sensitivity of 90% (95% CI, 76–100%) and specificity of 96% (95% CI, 40%–99%) for stage I OC. While CA125 have an overall sensitivity for all stages of OC of 61% (95% CI, 53–69%), with a sensitivity of 44% for stage I (95% CI, 28–62%). Conclusions: In this study we report the development and validation of an accurate, automated high-throughput EV-based test for early detection of ovarian cancer. The test delivers significant improvements in sensitivity and specificity compared to CA-125, especially in detecting early-stage OC.

With the advent of multi-layered and 3D scaffolds, the understanding of microbiome composition and pathogenic mechanisms within polymicrobial biofilms is continuously evolving. A fundamental component in mediating the microenvironment and bacterial-host communication within the biofilm are bilayered nanoparticles secreted by bacteria, known as bacterial extracellular vesicles (BEVs), which transport key biomolecules including proteins, nucleic acids, and metabolites. Their characteristics and microbiome profiles are yet to be explored in the context of in vitro salivary polymicrobial biofilm. This pilot study aimed to compare the profiles of BEVs from salivary biofilm cultured on a 2D tissue culture plate and 3D melt electrowritten medical-grade polycaprolactone (MEW mPCL) scaffold. BEVs derived from MEW mPCL biofilm exhibited enhanced purity and yield without altered EV morphology and lipopolysaccharide (LPS) content, with enriched BEVs-associated DNA from Capnocytophaga, porphyromonas, and veillonella genus. Moreover, compared to saliva controls, MEW mPCL BEVs showed comparable DNA expression of Tannerella forsythia , and Treponema denticola and significantly higher expression in Porphyromonas gingivalis, Eikenella corrodens and Lactobacillus acidophilus . Together, these findings highlight a more detailed microbial profile with BEVs derived from salivary biofilms cultured on 3D MEW PCL scaffolds, which facilitates an effective in vitro model with a greater resemblance to naturally occurring biofilms.

Gestational diabetes mellitus (GDM) affects 2–20

BACKGROUND:The current method for diagnosing endometrial cancer and monitoring treatment response is invasive and fraught with both interobserver and intraobserver variability. Less invasive and more reproducible methods are desirable. This systematic review examines the evidence for extracellular vesicles as minimally invasive biomarkers for endometrial cancer. METHODS:PubMed, Embase and Web of Science were searched for studies reporting extracellular vesicles as biomarkers in females with endometrial cancer. Risk of bias was assessed using the QUADAS-2 tool. A descriptive synthesis of biomarkers reported in the included studies was conducted. RESULTS:Of the 680 unique records reviewed, 23 studies were included. All 23 studies investigated extracellular vesicles as diagnostic biomarkers. Ten extracellular vesicle-associated biomarkers were consistently reported to be differentially abundant between endometrial cancer cases and controls in multiple independent studies and hence may be putative diagnostic biomarkers. Levels of extracellular vesicles, LGALS3BP (galectin 3 binding protein), miR-15a-5p, and miR-21-3p were elevated, while levels of miR-26a-5p, miR-130a-3p, miR-139, miR-219a-5p, miR-222-3p, and miR-885 were decreased in endometrial cancer cases versus controls. Seven studies also investigated extracellular vesicles as prognostic biomarkers, but no biomarker was reported as prognostic in more than one study. CONCLUSION:Of the ten putative diagnostic biomarkers, extracellular vesicle-associated miR-21-3p, miR-26a-5p, miR-130a-3p, miR-139 and miR-219a-5p are the most promising as their expression in extracellular vesicle preparations appears to reflect that in endometrial tissue. However, there are significant concerns regarding study quality, limited adherence to consensus recommendations on extracellular vesicle research and lack of evidence supporting biomarkers being encapsulated within extracellular vesicles.

BACKGROUND:Preeclampsia is a challenging pregnancy disorder to treat, and current preventive measures are not always effective. Aspirin is prescribed as a preventive agent for pregnant women who are at high risk of developing preeclampsia although there is no definitive conclusion for its mechanism of action or efficacy. Extracellular vesicles (EVs) provide a picture of the physiological state of the cells from which they originate and have been implicated in both normal and pathological pregnancies. This study reviewed the potential effects of aspirin on EVs when prescribed for prevention of preeclampsia. METHODS:The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to identify published studies from the CENTRAL, The Cochrane Library (Cochrane Central Register of Controlled Trials), MEDLINE (Medical Literature Analysis and Retrieval System online) on PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and Embase (Excerpta Medica Database) from inception to March 2024. RESULTS:Sixty-three studies met the inclusion criteria and were grouped as studies on aspirin-mediated prevention of preeclampsia (n=31), studies on EVs in the pathogenesis of preeclampsia (n=28), and studies on the effects of aspirin on EVs in preeclampsia (n=4). Meta-analysis of randomized control trials showed that the odds of preeclampsia occurrence is 36% less likely in the aspirin treatment group (odds ratio, 0.64 [95% CI, 0.47-0.87]; P<0.01). EVs are involved in the pathogenesis of preeclampsia, and aspirin effectively reduced the negative effects of EVs in both in vitro and in vivo studies. CONCLUSIONS:Aspirin has an inhibitory effect on EVs in preeclampsia. However, further studies are needed to understand and confirm the mechanisms involved.

Non-alcoholic fatty liver disease (NAFLD) is a liver disorder that has become a global health concern due to its increasing prevalence. There is a need for reliable biomarkers to aid in the diagnosis and prognosis of NAFLD. Extracellular vesicles (EVs) are promising candidates in biomarker discovery, as they carry proteins that reflect the pathophysiological state of the liver. In this review, we developed a list of EV proteins that could be used as diagnostic biomarkers for NAFLD. We employed a multi-step strategy that involved reviewing and comparing various sources of information. Firstly, we reviewed papers that have studied EVs proteins as biomarkers in NAFLD and papers that have studied circulating proteins as biomarkers in NAFLD. To further identify potential candidates, we utilized the EV database Vesiclepedia.org to qualify each protein. Finally, we consulted the Human Protein Atlas to search for candidates' localization, focusing on membrane proteins. By integrating these sources of information, we developed a comprehensive list of potential EVs membrane protein biomarkers that could aid in diagnosing and monitoring NAFLD. In conclusion, our multi-step strategy for identifying EV-based protein biomarkers for NAFLD provides a comprehensive approach that can also be applied to other diseases. The protein candidates identified through this approach could have significant implications for the development of non-invasive diagnostic tests for NAFLD and improve the management and treatment of this prevalent liver disorder.

Abstract Ovarian Cancer (OC) is the most common gynaecological malignancy and the eighth most diagnosed cancer in females worldwide. Currently, it is the fifth leading cause of cancer-related mortality among patients globally, largely due to delayed diagnosis, chemotherapy resistance, high metastasis rates, and subtype heterogeneity. Recent OC research highlights extracellular vesicles (EVs) as pivotal contributors to intercellular communication and disease progression, elucidating the intricacies of OC pathology. EVs, diverse membrane-derived vesicles released by most cells, carry molecular cargoes containing proteins and nucleic acids. Studies indicate that the biogenesis, packaging, and release of EVs are highly dependent and sensitive to the cellular microenvironment and depend on the in-vitro and in-vivo modelling systems. Labs use varied model systems—2D monolayers, animal models, and innovative 3D models—to investigate EVs' ovarian cancer roles. Therefore, in this study we aimed to compare the miRNA profiles associated with EVs in 3D ovarian cancer cell models, and to identify the pathophysiological relevance of the EVs isolated from these models to the patient derived EVs.In this study, two OC epithelial cell lines, SKOV-3 and OVCAR-, were initially cultured as 2D monolayers and embedded within Gelatin Methacryloyl hydrogels. Over nine days, multiple assays observed spheroid formation within the cell-laden hydrogels. EVs isolated from the cell-conditioned media were characterized per MISEV 2018 guidelines. Small RNA sequencing identified statistically significant miRNAs, subject to gene ontology and gene rank analyses. EVs from a cohort of 60 OC patients were used to identify survival-associated miRNA profiles.Our findings in the cell-laden hydrogels demonstrated OC cell growth, proliferation, and aggregation into spheroidal structures, establishing an ideal 3D model. The isolated EVs were characterized for size, concentration, morphology, and surface markers. Small RNA sequencing revealed 18 significantly different EV-associated miRNA species across 3D vs 2D models, influencing apoptosis, angiogenesis, migration, and proliferation in ovarian cancer. Notably, 3D model-derived EV-associated miRNAs mirrored patient-derived EV-associated miRNA profiles, indicating their pathophysiological relevance.This study establishes a robust 3D OC model in hydrogels, showcasing growth, proliferation, and aggregation capabilities. Differential miRNA profiles between 3D and 2D model EVs highlight the critical roles of these miRNAs in essential ovarian cancer processes. Moreover, similarities between 3D model and patient-derived EV-associated miRNA profiles emphasize the clinical relevance of our model. Citation Format: Nihar Godbole, Akhilandeshwari Ravichandran, Dominic Guanzon, Andrew Lai, Flavio Carrion, Priyakshi Kalita de Croft, Lewis Perrin, John Hooper, Laura Bray, Carlos Salomon. Changes in extracellular vesicle miRNAs from three-dimensional ovarian cancer cell models reflect physiological changes and cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3386.

Bacterial extracellular vesicles (BEVs) are naturally occurring bioactive membrane-bound nanoparticles released by both gram-negative and positive bacterial species, exhibiting a multifaceted role in mediating host-microbe interactions across various physiological conditions. Increasing evidence supports BEVs as essential mediators of intercellular exchange, influencing bacterial pathogenicity, disease mechanisms, and modulating the host immune response. However, the extent to which these BEV-mediated actions can be leveraged to predict disease onset, guide treatment strategies, and determine clinical outcomes remains uncertain, particularly in terms of their clinical translation potentials. This review briefly describes BEV biogenesis and their internalisation by recipient cells and summarises methods for isolation and characterization, essential for understanding their composition and cargo. Further, it discusses the potential of biofluid-associated BEVs as biomarkers for various diseases, spanning both cancer and non-cancerous conditions. Following this, we also outline the ongoing human clinical trials of using BEVs for vaccine development. In addition to disease diagnostics, this review explores the emerging research of using natural or engineered BEVs as smart nanomaterials for applications in anti-cancer therapy and bone regeneration. This discussion extends to key factors for unlocking the clinical potential of BEVs, such as standardization of BEVs isolation and characterisation, as well as other hurdles in translating these findings to the clinical setting. We propose that addressing these hurdles through collaborative research efforts and well-designed clinical trials holds the key to fully harnessing the clinical potential of BEVs. As this field advances, this review suggests that BEV-based nanomedicine has the potential to revolutionize disease management, paving the way for innovative diagnosis, therapeutics, and personalized medicine approaches.Statement of SignificanceExtracellular vesicles (EVs) from both host cells and bacteria serve as multifunctional biomaterials and are emerging in the fields of biomedicine, bioengineering, and biomaterials. However, most of the current studies focus on host-derived EVs, leaving a gap in comprehensive research on bacteria-derived EVs (BEVs). Although BEVs offer an attractive option as nanomaterials for drug delivery systems, their unique nanostructure and easy-to-modify functions make them a potential method for disease diagnosis and treatment as well as vaccine development. Our work among the pioneering studies investigating the potential of BEVs as natural nanobiomaterials, plays a crucial role in both understanding the development of diseases and therapeutic interventions.

Abstract Objectives Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) virus infection in pregnancy is associated with higher incidence of placental dysfunction, referred to by a few studies as a ‘preeclampsia‐like syndrome’. However, the mechanisms underpinning SARS‐CoV‐2‐induced placental malfunction are still unclear. Here, we investigated whether the transcriptional architecture of the placenta is altered in response to SARS‐CoV‐2 infection. Methods We utilised whole‐transcriptome, digital spatial profiling, to examine gene expression patterns in placental tissues from participants who contracted SARS‐CoV‐2 in the third trimester of their pregnancy (n = 7) and those collected prior to the start of the coronavirus disease 2019 (COVID‐19) pandemic (n = 9). Results Through comprehensive spatial transcriptomic analyses of the trophoblast and villous core stromal cell subpopulations in the placenta, we identified SARS‐CoV‐2 to promote signatures associated with hypoxia and placental dysfunction. Notably, genes associated with vasodilation (NOS3), oxidative stress (GDF15, CRH) and preeclampsia (FLT1, EGFR, KISS1, PAPPA2) were enriched with SARS‐CoV‐2. Pathways related to increased nutrient uptake, vascular tension, hypertension and inflammation were also enriched in SARS‐CoV‐2 samples compared to uninfected controls. Conclusions Our findings demonstrate the utility of spatially resolved transcriptomic analysis in defining the underlying pathogenic mechanisms of SARS‐CoV‐2 in pregnancy, particularly its role in placental dysfunction. Furthermore, this study highlights the significance of digital spatial profiling in mapping the intricate crosstalk between trophoblasts and villous core stromal cells, thus shedding light on pathways associated with placental dysfunction in pregnancies with SARS‐CoV‐2 infection.

The field of extracellular vesicle (EV) signalling has the potential to transform our understanding of maternal-fetal communication and affords new opportunities for non-invasive prenatal testing and therapeutic intervention. EVs have been implicated in implantation, placentation, maternal adaptation to pregnancy and complications of pregnancy, being detectable in maternal circulation as early as 6 weeks of pregnancy. EVs of differing biogenic origin, composition and bioactivity are released by cells to maintain homoeostasis. Induction of EV signalling is associated with aberrant cellular metabolism and manifests as changes in EV concentrations and/or composition. Characterizing such changes affords opportunity to develop more informative diagnostics and efficacious interventions. To develop accurate and reliable EV-based diagnostics requires: identification of disease-associated biomarkers in specific EV subpopulations; and rapid, reproducible and scalable sample processing. Conventional isolation methods face challenges due to co-isolation of particles with similar physicochemical properties. Methods targeting specific vesicle-surface epitopes and compatible with automated platforms show promise. Effective EV therapeutics require precise targeting, achieved through genetic engineering to release EVs expressing cell-targeting ligands and carrying therapeutic payloads. Unlike cell-based therapies, this approach offers advantages including: low immunogenicity; stability; and long-term storage. Although EV diagnostics and therapeutics in reproductive biology are nascent, available technologies can enhance our understanding of EV signalling between mother and fetus, its role in pregnancies and improve outcomes.

Extracellular vesicles (EVs), including exosomes, have significant potential for diagnostic and therapeutic applications. The lack of standardized methods for efficient and high-throughput isolation and analysis of EVs, however, has limited their widespread use in clinical practice. Surface epitope immunoaffinity (SEI) isolation utilizes affinity ligands, including antibodies, aptamers, or lectins, that target specific surface proteins present on EVs. Paramagnetic bead-SEI isolation represents a fit-for-purpose solution for the reproducible, high-throughput isolation of EVs from biofluids and downstream analysis of RNA, protein, and lipid biomarkers that is compatible with clinical laboratory workflows. This study evaluates a new SEI isolation method for enriching subpopulations of EVs. EVs were isolated from human plasma using a bead-based SEI method designed for on-bead and downstream analysis of EV-associated RNA and protein biomarkers. Western blot analysis confirmed the presence of EV markers in the captured nanoparticles. Mass spectrometry analysis of the SEI lysate identified over 1500 proteins, with the top 100 including known EV-associated proteins. microRNA (miRNA) sequencing followed by RT-qPCR analysis identified EV-associated miRNA transcripts. Using SEI, EVs were isolated using automated high-throughput particle moving instruments, demonstrating equal or higher protein and miRNA yield and recovery compared to manual processing. SEI is a rapid, efficient, and high-throughput method for isolating enriched populations of EVs; effectively reducing contamination and enabling the isolation of a specific subpopulation of EVs. In this study, high-throughput EV isolation and RNA extraction have been successfully implemented. This technology holds great promise for advancing the field of EV research and facilitating their application for biomarker discovery and clinical research.

In the original publication [...].

CONTEXT:Pancreatic β-cell function impairment is a key mechanism for developing gestational diabetes mellitus (GDM). Maternal and placental exosomes regulate maternal and placental responses during hyperglycemia. Studies have associated exosomal micro-RNAs (miRNAs) with GDM development. To date, no studies have been reported that evaluate the profile of miRNAs present in maternal and placental exosomes in the early stages of gestation from pregnancies that develop GDM. OBJECTIVE:We assessed whether early-pregnancy serum maternal and placenta-derived exosomes miRNA profiles vary according to pancreatic β-cell function in women who will develop GDM. METHODS:A prospective nested case-control study was used to identify exosomal miRNAs that vary in early-pregnancy stages (<18 weeks of gestation) from women with normoglycemia and those who developed GDM based on their pancreatic β-cell function using the homeostasis model assessment of pancreatic β-cell function (HOMA-%β) index. Early-pregnancy serum maternal and placenta-derived exosomes were isolated to obtain miRNA profiles. Potential target and pathway analyses were performed to identify molecular and metabolic pathways associated with the exosomal miRNAs identified. RESULTS:In early-pregnancy stages, serum maternal exosome size and concentration are modified in GDM group and fluctuate according to HOMA-%β index. Serum maternal exosomal hsa-miR-149-3p and hsa-miR-455-3p in GDM are related to insulin secretion and signaling, lipolysis, and adipocytokine signaling. Early-pregnancy serum placenta-derived exosomes hsa-miR-3665 and hsa-miR-6727-5p in GDM are related to regulating genes involved in response to immunological tolerance of pregnancy and pathways associated with placental dysfunction. CONCLUSION:Early serum exosomal miRNAs differ depending on their origin (maternal or placental) and pancreatic β-cell function. This research provides insights into the interactions between maternal and placental exosomal miRNAs and may have implications for identifying potential biomarkers or therapeutic targets for GDM.

Sweat contains numerous vital biomarkers such as metabolites, electrolytes, proteins, nucleic acids and antigens that reflect hydration status, exhaustion, nutrition, and physiological changes. Conventional healthcare diagnosis relies on disease diagnostics in sophisticated centralized laboratories with invasive sample collection (e.g., chemical analyses, plasma separation via centrifugation, tissue biopsy, etc.). Cutting-edge point-of-care diagnostics for sweat biomarker analysis allow for non-invasive monitoring of physiologically related biomarkers in sweat and real-time health status tracking. Moreover, using advanced nanoarchitectures, including nanostructured platforms and nanoparticles, can enhance the specificity, sensitivity, wearability and widen the sensing modality of sweat biosensors. Herein, we comprehensively review the secretory mechanisms, clinical uses of sweat biomarkers, and the design, principle, and latest technologies of sweat biosensors. With an emphasis on cutting-edge technologies for sweat biomarker analysis, this review chronicles the issues associated with the current sweat biomarkers analysis of sweat biomarkers and provides insights into strategies for enhancing the translation of such biosensors into routine clinical practice.

AbstractPlacental extracellular vesicles (EVs) can be found in the maternal circulation throughout gestation, and their concentration, content and bioactivity are associated with pregnancy outcomes, including gestational diabetes mellitus (GDM). However, the effect of changes in the maternal microenvironment on the mechanisms associated with the secretion of EVs from placental cells remains to be fully established. Here, we evaluated the effect of high glucose on proteins associated with the trafficking and release of different populations of EVs from placental cells. BeWo and HTR8/SVneo cells were used as placental models and cultured under 5‐mM D‐glucose (i.e. control) or 25‐mM D‐glucose (high glucose). Cell‐conditioned media (CCM) and cell lysate were collected after 48 h. Different populations of EVs were isolated from CCM by ultracentrifugation (i.e. pellet 2K‐g, pellet 10K‐g, and pellet 100K‐g) and characterised by Nanoparticle Tracking Analysis. Quantitative proteomic analysis (IDA/SWATH) and multiple reaction monitoring protocols at high resolution (MRMHR) were developed to quantify 37 proteins related to biogenesis, trafficking/release and recognition/uptake of EVs. High glucose increased the secretion of total EVs across the pellets from BeWo cells, an effect driven mainly by changes in the small EVs concentration in the CCM. Interestingly, no effect of high glucose on HTR8/SVneo cells EVs secretion was observed. High glucose induces changes in proteins associated with vesicle trafficking in BeWo cells, including Heat Shock Protein Family A (Hsp70) Member 9 (HSPA9) and Member 8 (HSPA8). For HTR8/SVneo, altered proteins including prostaglandin F2α receptor regulatory protein (FPRP), RAB5A, RAB35, RAB5B, and RB11B, STAM1 and TSG101. These proteins are associated with the secretion and trafficking of EVs, which could explain in part, changes in the levels of circulating EVs in diabetic pregnancies. Further, we identified that proteins RAB11B, PDCD6IP, STAM, HSPA9, HSPA8, SDCBP, RAB5B, RAB5A, RAB7A and ERAP1 regulate EV release in response to high and low glucose when overexpressed in cells. Interestingly, immunohistochemistry analysis of RAB7A revealed distinct changes in placental tissues obtained from women with normal glucose tolerance (NGT, n = 6) and those with GDM (n = 6), influenced by diet or insulin treatment. High glucose regulation of proteins involved in intercellular dynamics and the trafficking of multivesicular bodies to the plasma membrane in placental cells is relevant in the context of GDM pregnancies.