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e. piccione 2,7 , e. Zupi 6 & A. pietropolli 2 RPL is a very debated condition, in which many issues concerning definition, etiological factors to investigate or therapies to apply are still controversial. ML could help clinicians to reach an objectiveness in RPL classification and access to care. Our aim was to stratify RPL patients in different risk classes by applying an ML algorithm, through a diagnostic work-up to validate it for the appropriate prognosis and potential therapeutic approach. 734 patients were enrolled and divided into 4 risk classes, according to the numbers of miscarriages. ML method, called Support Vector Machine (SVM), was used to analyze data. Using the whole set of 43 features and the set of the most informative 18 features we obtained comparable results: respectively 81.86 ± 0.35% and 81.71 ± 0.37% Unbalanced Accuracy. Applying the same method, introducing the only features recommended by ESHRE, a correct classification was obtained only in 58.52 ± 0.58%. ML approach could provide a Support Decision System tool to stratify RPL patients and address them objectively to the proper clinical management. Recurrent pregnancy loss (RPL) is a very debated field: the absence of fully shared guidelines arises controversial issues in the clinical management of these patients, confusion about diagnostic work-up to be performed and potential therapies to be applied. This confusion is attributable to the impossibility of reaching an evidence-based-medicine, since the disparity, even on the definition of the pathology, makes the approach to the problem more complicated 1. Furthermore, in the scientific community, no full agreement has been achieved on crucial factors, such as number of miscarriages to be considered for the definition (two versus three); consecutivity of miscarriage; inclusion in the definition of RPL of: "non-visualized pregnancy losses", biochemical abortions, intrauterine clinical abortions, pregnancies of unknown location (PULs), ectopic or molar pregnancies. The most recent international guidelines of the ESHRE (European Society of Human Reproduction and Embriology) define RPL as the loss of two or more pregnancies before the 24th week of gestation. With this settlement, the concept of consecutivity lapses and "non-visual" pregnancies, such as biochemical or PULs abortions,

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

BackgroundThe incidence of hepatocellular carcinoma (HCC) and its association with hepatitis C (HCV) and hepatitis B virus (HBV) infections, FIB-4 index and liver enzymes was assessed in an area of the province of Naples covered by a population-based cancer registry.MethodsWe conducted a cohort investigation on 4492 individuals previously enrolled in a population-based seroprevalent survey on HCV and HBV infections. The diagnosis of HCC was assessed through a record linkage with the cancer registry. Hepatic metabolic activity was measured through serum alanine transaminase, aspartate aminotransferase, gamma-glutamyl-transferase, and platelet. The FIB-4 index was used as a marker of fibrosis. We computed HCC incidence rates (IR) for 100,000 (105) person-years of observation, and multivariable hazard ratios (HR) with 95 % confidence intervals (CI) to assess risk factors for HCC.ResultsTwenty two cases of HCC were diagnosed during follow-up (IR = 63.3 cases/105). Significantly increased HCC risks were documented in individuals with higher than normal liver enzymes and low platelet count; in the 239 HCV RNA-positives (HR = 61.8, 95 % CI:13.3–286); and in the 95 HBsAg-positives (HR = 75.0) –as compared to uninfected individuals. The highest FIB-4 score was associated with a 17.6-fold increased HCC risk.ConclusionsAn elevated FIB-4 index turned out to be an important predictor of HCC occurrence. Although the standard method to assess hepatic fibrosis in chronic hepatitis remains the histologic staging of liver biopsy specimen, the assessment of FIB-4 in HCV RNA-positive individuals may help in identifying the highest HCC-risk individuals who need anti-HCV treatment most urgently.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial–temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of “social forces” from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations via cooperative learning approaches, discriminating with high accuracy prostate noncancer vs. cancer cells of high vs. low malignancy. Comparison with standard classification methods validates our approach, which therefore represents a promising tool for addressing clinically relevant issues in cancer diagnosis and therapy, e.g., detection of potentially metastatic cells and anticancer drug screening.