The precise monitoring of environmental contaminants and agricultural plant stress factors, respectively responsible for damages to our ecosystems and crop losses, has nowadays become a topic of uttermost importance. This is also highlighted by the recent introduction of the so-called “Sustainable Development Goals” of the United Nations, which aim at reducing pollutants while implementing more sustainable food production practices, leading to a reduced impact on all ecosystems. In this context, the standard methods currently used in these fields represent a sub-optimal solution, being expensive, laboratory-based techniques, and typically requiring trained personnel with high expertise. Recent advances in both biotechnology and material science have led to the emergence of new sensing (and biosensing) technologies, enabling low-cost, precise, and real-time detection. An especially interesting category of biosensors is represented by field-effect transistor-based biosensors (bio-FETs), which enable the possibility of performing in situ, continuous, selective, and sensitive measurements of a wide palette of different parameters of interest. Furthermore, bio-FETs offer the possibility of being fabricated using innovative and sustainable materials, employing various device configurations, each customized for a specific application. In the specific field of environmental and agricultural monitoring, the exploitation of these devices is particularly attractive as it paves the way to early detection and intervention strategies useful to limit, or even completely avoid negative outcomes (such as diseases to animals or ecosystems losses). This review focuses exactly on bio-FETs for environmental and agricultural monitoring, highlighting the recent and most relevant studies. First, bio-FET technology is introduced, followed by a detailed description of the the most commonly employed configurations, the available device fabrication techniques, as well as the specific materials and recognition elements. Then, examples of studies employing bio-FETs for environmental and agricultural monitoring are presented, highlighting in detail advantages and disadvantages of available examples. Finally, in the discussion, the major challenges to be overcome (e.g., short device lifetime, small sensitivity and selectivity in complex media) are critically presented. Despite the current limitations and challenges, this review clearly shows that bio-FETs are extremely promising for new and disruptive innovations in these areas and others.

The demand for user-friendly applications to support biologists in analyzing high-throughput proteomics data remains a pressing challenge. Given the complexity and the multiple intermediate steps involved, this process is time-consuming and often requires specialized computational skills. To simplify and accelerate the exploration of proteomics data, we present PiProteline, an R package designed to operate on high-dimensional data matrices assembled from the output of any search engine commonly used in bottom-up proteomics experiments. In addition to data preprocessing and descriptive statistics, PiProteline enables label-free quantitation, functional enrichment, and systems biology analyses. Notably, it supports both unweighted and weighted protein-protein interaction (PPI) network topological analyses for the identification of critical nodes, such as hubs and bottlenecks. By integrating multiple analytical approaches, PiProteline accelerates the selection of potentially relevant protein signatures, providing insights into the molecular mechanisms characterizing the investigated systems. This information facilitates the formulation of new hypotheses and the design of targeted experiments, ultimately reducing costs and advancing translational medicine. PiProteline is available as an open-source R package via GitHub (https://github.com/lomi95/PiProteline) and as a Shiny application (https://github.com/salehnhd/PiProteline-Shiny).

Immunoglobulin light chains (AL) and transthyretin (ATTR) characterize most cardiac amyloidosis cases. Several proteomic studies have analyzed microdissected amyloid plaques. However, they did not capture the molecular dysfunctions triggered in surrounding tissue. We, therefore, holistically investigated the protein profiles of endomyocardial biopsies from patients with ALλ and ATTR conditions. Both conditions showed extracellular matrix remodeling and downregulation of proteostasis, signaling, and mitochondrial energetics. The results also suggested vesicle-mediated transport and complement activation as hallmarks of patients with ALλ and ATTR conditions, respectively. These findings corroborate previously proposed pathological mechanisms, namely, direct mitochondrial toxicity of light chain fibrils internalized via macropinocytosis and massive extracellular deposition of ATTR. Notably, protein quantification and network topology enabled the identification of potential disease markers. Although the selection criteria were robust, validation in larger cohorts is needed. Nevertheless, given the challenging accessibility of the analyzed samples, the proposed landscape of results provides a valuable resource to encourage further studies on cardiac amyloidosis.

The demand for user-friendly applications to support biologists in analyzing high-throughput proteomics data remains a pressing challenge. Given the complexity and the multiple intermediate steps involved, this process is time-consuming and often requires specialized computational skills. To simplify and accelerate the exploration of proteomics data, we present PiProteline, an R package designed to operate on high-dimensional data matrices assembled from the output of any search engine commonly used in bottom-up proteomics experiments. In addition to data preprocessing and descriptive statistics, PiProteline enables label-free quantitation, functional enrichment, and systems biology analyses. It supports both unweighted and weighted protein–protein interaction (PPI) network topological analyses for the identification of critical nodes, such as hubs and bottlenecks. By integrating multiple computational approaches, PiProteline accelerates the selection of potentially relevant protein signatures, providing insights into the molecular mechanisms characterizing the investigated systems. This information facilitates the formulation of new hypotheses and the design of targeted experiments, ultimately reducing costs and advancing translational medicine. PiProteline is available as an open-source R package via GitHub, as a Shiny application, and via Zenodo.