The development of more sensitive methodologies, capable of quickly detecting and monitoring a microbial population present in a specific biological matrix, as well as performing to allow for the study of all its metabolic changes (e.g., during the formation of biofilm) to occur, is an essential requirement for both well-being and the food industry. Two techniques, in particular, have gained the attention of scientists: The first is “biospeckle”, an optical technique representing an innovative tool for applications in food quality, food safety, and nutraceuticals. With this technique, we can quickly evaluate and monitor the presence of bacteria (or their proliferation) in a solid or liquid biological matrix. In addition, the technique is helpful in quantifying and optimizing the correct storage time of the pro-biotics, if they are entrapped in matrices such as alginate and follow their survival rate in simulated gastro-intestinal conditions. A second technique with great chances is the “biofilm electrostatic test” (BET). BET undoubtedly represents a fast, simple, and highly reproducible tool suitable for admitting the evaluation of the in vitro bacterial capacity in order to adhere through an electrostatic interaction with a pyro-electrified carrier after only 2 h of incubation. BET could represent the way for a quick and standardized evaluation of bacterial resistance among biofilm-producing microorganisms through a fast evaluation of the potential presence of the biofilm.

The formation of intracellular vacuoles in mammalian cells is associated with different dysfunctions, e.g., autophagy, lysosomal storage diseases, and cellular stress. We introduce a pipeline for high-throughput, label-free analysis of adherent cells and vacuoles using Fourier ptychographic microscopy (FPM). In fact, by tailoring the Cellpose model to the case of FPM phase-contrast imaging, we segment and characterize more than 3 × 10 ⁴ cells and more than 10 ⁵ vacuoles. We tune the platform using cells engineered to express various types of vacuoles. In pathological cells, our platform can identify distinct subpopulations within the same patient-derived line. By analyzing their content, the platform could yield clues about their origin and machinery, as well as screen conditions highlighted by vacuole formations.

The incidence of Head and Neck Squamous Cell Carcinoma (HNSCC) has been growing in the last few decades. Its diagnosis is usually performed through clinical evaluation and analyzing radiological images, then confirmed by histopathological examination, an invasive and time-consuming operation. The recent advances in the artificial intelligence field are leading to interesting results in the early diagnosis, personalized treatment and monitoring of HNSCC only by analyzing radiological images, without performing a tissue biopsy. The large amount of radiological images and the increasing interest in radiomics approaches can help to develop machine learning (ML) methods to support diagnosis. In this work, we propose an ML method based on the use of radiomics features, extracted from CT and PET images, to classify the disease in terms of pN-Stage, pT-Stage and Overall Stage. After the extraction of radiomics features, a selection step is performed to remove dataset redundancy. Finally, ML methods are employed to complete the classification task. Our pipeline is applied on the “Head-Neck-PET-CT” TCIA open-source dataset, considering a cohort of 201 patients from four different institutions. An AUC of $97\%$, $83\%$ and $93\%$ in terms of pN-Stage, pT-Stage and Overall Stage classification, respectively, is achieved. The obtained results are promising, showing the potential efficiency of the use of radiomics approaches in staging classification.

The phenotyping of hereditary anemias remains a challenge in clinical diagnostics. Digital Holography allows exploiting a bunch of descriptive information of red blood cells that, in tandem with Machine Learning, accurately classify anemia’s type.