Beyond the Bench: Cutting-Edge Sensing and Identification of Emergent Environmental and Clinical Stressors

Dear Colleagues,

The global public health landscape is facing a critical inflection point as anthropogenic stressors, including micro/nanoplastics (MNPs), per- and polyfluoroalkyl substances (PFAS), antimicrobial resistance (AMR) markers, and endocrine-disrupting chemicals (EDCs), become ubiquitous in both environmental and clinical matrices. Traditional laboratory analytical methods, while accurate, are often high-cost, time-intensive, and restricted to centralized laboratories, creating a significant "blind spot" in real-time environment assessment and early clinical intervention.

This Special Issue, "Beyond the Bench," seeks to explore a paradigm shift in bioengineering where detection is moved directly to the point of need. We focus on the convergence of nanotechnology, microfluidics, and data-driven approaches, including Artificial Intelligence (AI) to create next-generation sensing architectures. By integrating Machine Learning (ML) and Deep Learning (DL), these next-generation systems can extract high-fidelity signals from complex, "noisy" samples such as untreated wastewater, soil extracts, and human biofluids (blood, saliva, urine). Notably, we emphasize not only detection but also the translation of sensing data into actionable insights for exposure assessment, risk evaluation, and early clinical decision-making. The goal of this issue is to highlight bioengineering innovations that not only detect these stressors but also provide predictive insights into their translocation from the environment to human biological systems. We particularly encourage contributions that bridge environmental monitoring with human health outcomes, enabling a more integrated understanding of exposure–response relationships.

We invite submissions that push the boundaries of current analytical frameworks, with a particular interest in integrated hardware-software co-design. Topics of interest include, but are not limited to:

• AI & Machine Learning: Deep learning for automated identification of MNPs and complex chemical stressors in "noisy" matrices.
• Non-Traditional Sensing: CRISPR-based, SERS-active, electrochemical, and bio-recognition-based biosensors for trace-level contaminant detection.
• Clinical-Environmental Interplay: Portable technologies capable of tracking stressors from environmental sources (water, soil) directly into human biofluids.
• Smart Lab-on-a-Chip: Autonomous, AI-integrated or sensor0integrated microfluidic devices for rapid on-site diagnostics.
• Advanced Data Analytics: AI algorithms for high-throughput screening and predictive toxicology of anthropogenic stressors
• Translation and Validation: Methods that demonstrate validation against conventional analytical techniques (e.g., GC-MS, LC-MS), standardization, or pathways toward regulatory acceptance and real-world deployment.

Dr. Longyan Chen
Dr. Yi Chen
Guest Editor

Dr. Liu Cao
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• microplastic detection
• nanoplastic detection
• polyfluoroalkyl substances (PFAS) detection
• anthropogenic stressors
• machine learning
• trace-level detection
• human biofluid analysis
• environmental health monitoring
• predictive toxicology
• high-throughput screening
• AI-driven sensing