Outer Membrane Vesicles (OMV) constitute a promising platform for the development of efficient vaccines. OMV can be decorated with heterologous antigens (proteins or polysaccharides), becoming attractive novel carriers for the development of multicomponent vaccines. Chemical conjugation represents a tool for linking antigens, also from phylogenetically distant pathogens, to OMV. Here we develop two simple and widely applicable conjugation chemistries targeting proteins or lipopolysaccharides on the surface of Generalized Modules for Membrane Antigens (GMMA), OMV spontaneously released from Gram-negative bacteria mutated to increase vesicle yield and reduce potential reactogenicity. A Design of Experiment approach was used to identify optimal conditions for GMMA activation before conjugation, resulting in consistent processes and ensuring conjugation efficiency. Conjugates produced by both chemistries induced strong humoral response against the heterologous antigen and GMMA. Additionally, the use of the two orthogonal chemistries allowed to control the linkage of two different antigens on the same GMMA particle. This work supports the further advancement of this novel platform with great potential for the design of effective vaccines.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic and so it is crucial the right evaluation of viral infection. According to the Centers for Disease Control and Prevention (CDC), the Real-Time Reverse Transcription PCR (RT-PCR) in respiratory samples is the gold standard for confirming the disease. However, it has practical limitations as time-consuming procedures and a high rate of false-negative results. We aim to assess the accuracy of COVID-19 classifiers based on Arificial Intelligence (AI) and statistical classification methods adapted on blood tests and other information routinely collected at the Emergency Departments (EDs). Methods: Patients admitted to the ED of Careggi Hospital from April 7th–30th 2020 with pre-specified features of suspected COVID-19 were enrolled. Physicians prospectively dichotomized them as COVID-19 likely/unlikely case, based on clinical features and bedside imaging support. Considering the limits of each method to identify a case of COVID-19, further evaluation was performed after an independent clinical review of 30-day follow-up data. Using this as a gold standard, several classifiers were implemented: Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), K-nearest neighbor (K-NN), Naive Bayes (NB). Results: Most of the classifiers show a ROC >0.80 on both internal and external validation samples but the best results are obtained applying RF, LR and NN. The performance from the external validation sustains the proof of concept to use such mathematical models fast, robust and efficient for a first identification of COVID-19 positive patients. These tools may constitute both a bedside support while waiting for RT-PCR results, and a tool to point to a deeper investigation, by identifying which patients are more likely to develop into positive cases within 7 days. Conclusions: Considering the obtained results and with a rapidly changing virus, we believe that data processing automated procedures may provide a valid support to the physicians facing the decision to classify a patient as a COVID-19 case or not.

Introduction: Recently, a flow cytometric (FC) based test has been developed for detection of circulating fetal cells to replace the less accurate and reproducible Kleihauer-Betke test. FC test is easier to perform, it can distinguish the origin of fetal cells, but it is expensive and available in highly specialized laboratories. We evaluated the introduction of high-performance liquid chromatography (HPLC) approach as initial screening to identify patients who need an additional FC test to better discriminate the nature of haemoglobin -F (HbF) positive cells. Methods: Blood samples from 130 pregnant women suspected to have fetomaternal haemorrhage were analysed with HPLC and FC methods. The cut-off for HbF HPLC concentration was calculated. Statistical analyses for the evaluation of HPLC as a screening method were performed. The positivity cut-off of HbF to be used as decision -making value to continue the investigation was calculated. Results: An excellent agreement (R- 2 > 0.90) was observed between the percentage of HbF obtained by HPLC and the percentage of fetal cells detected by FC. Results obtained from each assay were compared to define the HPLC threshold below which it is not necessary to continue the investigations, confirming the maternal nature of the HbF positive cells detected. Our study demonstrated that a cut-off of 1.0 % HbF obtained by HPLC was associated with the lowest rate of false negative results in our patient cohort. Conclusions: This study provides a new FMH investigation approach that possibly leads to a reduction in times and costs of the analysis.

Background/Objectives: Accurate post-mortem interval (PMI) estimation remains challenging in forensic pathology. Although potassium (K⁺) is the most well-validated single biomarker in vitreous humor (VH), multivariate approaches may enhance precision by capturing the complex cascade of post-mortem biochemical changes. This study aimed to develop and validate a multivariate PMI estimation model incorporating three biochemical markers—potassium, ammonium (NH₄⁺), and albumin (ALB)—in vitreous humor using automated clinical chemistry platforms for practical forensic application. Methods: Vitreous humor samples from 38 autopsy cases with documented PMIs (39.5–285 h; mean, 105.5 h) were analyzed for K⁺ (Cobas C8000), NH₄⁺ (Cobas C8000), and ALB (Immage 800 nephelometry). Univariate and multivariate regression analyses were performed, with the residual standard error (RSE) as the primary measure of accuracy. Model validation was conducted by back-calculating PMI in four samples completely distinct from the training cohort. Results: All three analytes demonstrated strong individual correlations with PMI (R²: K⁺ = 0.88, ALB = 0.78, NH₄⁺ = 0.69; all p < 0.001). The multivariate regression model [PMI = 40.25[Alb] + 0.01573[NH₄⁺] + 5.339[K⁺] − 53.032] yielded an RMSE of ±15.5 h (MSE = 240.25 h²), outperforming potassium-only models (RMSE = ±22.6 h). Although NH₄⁺ showed limited statistical significance in the multivariate model (p = 0.128), its inclusion improved overall predictive accuracy. External validation in an independent cohort of four subjects (distinct from the 38 subjects in the training set) demonstrated a mean absolute error (MAE) of 20.4 h. Conclusions: The multivariate approach combining K⁺, NH₄⁺, and ALB in VH improves PMI estimation accuracy compared with single-marker methods. The use of automated clinical chemistry platforms enhances reproducibility and facilitates practical implementation in forensic laboratories.

Serum protein electrophoresis (SPE) remains a central laboratory method for the detection and characterization of monoclonal gammopathies and other dysproteinemic patterns. Despite substantial analytical automation, interpretation still relies largely on graphical representation and expert visual assessment, making low-amplitude or borderline abnormalities particularly challenging. Artificial intelligence (AI), including machine-learning (ML) and deep-learning (DL) approaches, has increasingly been applied to SPE, with high performance reported for automated recognition of established abnormalities, particularly M-proteins, i.e., monoclonal immunoglobulins or immunoglobulin fragments produced by clonal plasma cells. However, most models have been developed against predefined diagnostic targets and evaluated using expert interpretation, immunofixation, or immunotyping as reference standards. Improved recognition of known abnormalities should therefore not be equated with discovery of additional clinically meaningful information. Modern electrophoretic platforms generate machine-readable numerical signals that may preserve features not explicitly considered during routine graphical interpretation. This creates the possibility of extending AI-assisted SPE from conventional pattern recognition toward detection of low-amplitude, distributed, or sub-visual signal characteristics. Yet current literature has focused predominantly on algorithmic performance, while the validation pathway required to determine whether additional AI-detected features are biologically meaningful and clinically actionable remains insufficiently defined. Evidence is strongest for automated M-protein recognition, including low-amplitude abnormalities, whereas non-canonical systemic patterns, longitudinal signal analysis, and AI-assisted decomposition of therapeutic monoclonal antibody interference remain exploratory. This critical review addresses this translational gap by evaluating AI applications according to the nature of detected information, strength of supporting evidence, and proximity to clinical implementation. We propose a translational framework in which additional AI-detected electrophoretic features progress through analytical validity, biological validity, clinical validity, and clinical utility before being considered actionable laboratory information.