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The introduction of immunotherapy has revolutionized the oncological targeted therapy paradigm. Microsatellite instability (MSI) identifies a subgroup of colorectal cancers (CRCs) which respond to treatment with immune checkpoint inhibitors. Tissue biopsy is currently the gold standard for the assessment of MSI/Mismatch Repair deficiency (MMRd) by means immunohistochemistry or molecular assays. However, the application of liquid biopsy in the clinic may help to overcome several limitations of tissue analysis and may provide great benefit to the diagnostic scenario and therapeutic decision-making process. In the context of MSI/MMRd CRC, the use of liquid biopsy may allow to establish MSI/MMR status if tissue sampling cannot be performed or in case of discordant tissue biopsies. Liquid biopsy may also become a powerful tool to monitor treatment response and the onset resistance to immunotherapy over time and to stratify of MSI/MMRd patients according to their risk of relapse and metastases. The aim of this review is to summarize the main technical aspects and clinical applications, the benefits, and limitations of the use of liquid biopsy in MSI/MMRd colorectal cancer patients., Competing Interests: UM has received personal fees (as consultant and/or speaker bureau) from Boehringer Ingelheim, Roche, MSD, Amgen, ThermoFisher Scientifics, Eli Lilly, Diaceutics, GSK, Merck, and AstraZeneca, Jannseen unrelated to the current work. SL has received personal fees for consulting or advisory role from Amgen, Merck Serono, Lilly, Astra Zeneca, Incyte, Daiichi-Sankyo, Bristol-Myers Squibb, Servier, MSD, Roche, Pierre-Fabre, and GSK, and receiving research grants from Amgen, Merck Serono, Bayer, Roche, Lilly, Astra Zeneca, and Bristol-Myers Squibb, unrelated to the current work. Matteo Fassan received research funding from Astellas Pharma, Macrophage Pharma and QED Therapeutics and had roles as consultant or advisor for Astellas Pharma, Roche, Astra Zeneca, MSD and GSK-Tesaro, unrelated to the current work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ceccon, Angerilli, Rasola, Procaccio, Sabbadin, Bergamo, Malapelle, Lonardi and Fassan.)

The introduction of next-generation sequencing (NGS) in the molecular diagnostic armamentarium is deeply changing pathology practice and laboratory frameworks. NGS allows for the comprehensive molecular characterization of neoplasms, in order to provide the best treatment to oncologic patients. On the other hand, NGS raises technical issues and poses several challenges in terms of education, infrastructures and costs. The aim of this review is to give an overview of the main NGS sequencing platforms that can be used in current molecular diagnostics and gain insights into the clinical applications of NGS in precision oncology. Hence, we also focus on the preanalytical, analytical and interpretative issues raised by the incorporation of NGS in routine pathology diagnostics.

The problem of detecting and identifying sensor faults is critical for efficient, safe, regulatory-compliant, and sustainable operations of modern industrial processing systems. The increasing complexity of such systems brings, however, new challenges for sensor fault detection and sensor fault isolation (SFD-SFI). One of the key enablers for any SFD-SFI method is analytical redundancy, which is provided by an analytical model of sensor observations derived from first principles or identified from historical data. As defective sensors generate measurements that are inconsistent with their expected behavior as defined by the model, SFD amounts to the generation and monitoring of residuals between sensor observations and model predictions. In this article, we introduce a disentangled recurrent neural network (RNN) with the objective to cope with the smearing-out effect, i.e., where the propagation of a sensor fault to nonfaulty sensor results in large and misleading residuals. The introduction of a probabilistic model for the residual generation allows us to develop a novel procedure for the identification of the faulty sensors. The computational complexity of the proposed algorithm is linear in the number of sensors as opposed to the combinatorial nature of the SFI problem. Finally, we empirically verify the performance of the proposed SFD-SFI architecture using a real data set collected at a petrochemical plant.

MicroRNAs (miRNAs) are small non-coding RNAs that play a pivotal role in many aspects of cell biology, including cancer development. Within esophageal cancer, miRNAs have been proved to be involved in all phases of carcinogenesis, from initiation to metastatic spread. Several miRNAs have been found to be dysregulated in esophageal premalignant lesions, namely Barrett's esophagus, Barrett's dysplasia, and squamous dysplasia. Furthermore, numerous studies have investigated the alteration in the expression levels of many oncomiRNAs and tumor suppressor miRNAs in esophageal squamous cell carcinoma and esophageal adenocarcinoma, thus proving how miRNAs are able modulate crucial regulatory pathways of cancer development. Considering these findings, miRNAs may have a role not only as a diagnostic and prognostic tool, but also as predictive biomarker of response to anti-cancer therapies and as potential therapeutic targets. This review aims to summarize several studies on the matter, focusing on the possible diagnostic-therapeutic implications.