Contamination of food with heavy metals is an important factor leading to serious health concerns. Rapid identification of these heavy metals is of utmost priority. There are several methods to identify traces of heavy metals in food. Conventional methods for the detection of heavy metal residues have their limitations in terms of cost, analysis time, and complexity. In the last decade, voltammetric analysis has emerged as the most prominent electrochemical determination method for heavy metals. Voltammetry is a reliable, cost-effective, and rapid determination method. This review provides a detailed primer on recent advances in the development and application of graphene-based electrochemical sensors for heavy metal monitoring over the last decade. We critically examine aspects of graphene modification (fabrication process, stability, cost, reproducibility) and analytical properties (sensitivity, selectivity, rapid detection, lower detection, and matrix effects) of these sensors. Furthermore, to our knowledge, meta-analyses were performed for the first time for all investigated parameters, categorized based on graphene materials and heavy metal types. We also examined the pass–fail criteria according to the WHO drinking water guidelines. In addition, the effects of heavy metal toxicity on human health and the environment are discussed. Finally, the contribution of heavy metal contamination to the seventeen Sustainable Development Goals (SDGs) stated by the United Nations in 2015 is discussed in detail. The results confirm the significant impact of heavy metal contamination across twelve SDGs. This review critically examines the existing knowledge in this field and highlights significant research gaps and future opportunities. It is intended as a resource for researchers working on graphene-based electrochemical sensors for the detection of heavy metals in food safety, with the ultimate goal of improving consumer health protection.

The detection of environmental toxicants is transitioning from centralized laboratory methods to decentralized, point-of-care (POC) monitoring. A highly innovative approach in this field is the repurposing of commercially available, low-cost, and portable personal glucose meters (PGMs) as universal biosensing platforms. This strategy leverages the widespread availability and ease of use of PGMs to develop rapid, on-site detection methods for a wide array of non-glucose targets, significantly reducing both cost and development time. This systematic review comprehensively examines the various strategies employed to adapt PGMs for the detection of a wide array of ecotoxicants, including chemical targets (antibiotics, mycotoxins, pesticides, heavy metals, persistent organic pollutants) and biological ones (pathogenic bacteria, and viruses). The systematic review critically evaluates different sensor designs, highlighting that while aptamer-based and non-enzymatic biosensors offer advantages in stability and cost, antibody-based sensors provide high specificity. A significant finding is the persistent trade-off between analytical sensitivity and practical field deployment; many of the most sensitive assays require multi-step procedures, precise temperature control, magnetic separation, centrifugation, and the use of additional equipment, factors that undermine true POC utility. To address this gap, we propose four essential criteria for POC readiness: (i) ambient-temperature operation, (ii) no reliance on magnetic or centrifugal separation, (iii) total assay time, and (iv) robustness in complex environmental matrices. This systematic review confirms the feasibility of this approach across a broad spectrum of targets. However, the key challenge for future research lies in simplifying the assay protocols, eliminating cumbersome sample preparation steps, and enhancing robustness to make these biosensors truly practical for routine, on-site environmental monitoring.

The increasing use of antibiotics around the globe has contributed to an increase in antimicrobial resistance and become a major risk to both public health and sustainable development. Reliable and fast detection of antibiotic residues in clinical, agricultural, and environmental matrices is required to monitor antimicrobial resistance effectively. The conventional analytical techniques are sensitive, but they are also expensive, complex and lacking in portability. Voltammetry is a recently emerging electrochemical detection technique that is low-cost and rapid. To the best of our knowledge, for the first time, a meta-analysis was conducted on graphene-based electrochemical sensors and biosensors for antibiotic detection over the last decade. This systematic review critically examines the analytical properties of sensors and biosensors, the physicochemical properties of antibiotics, adsorption characteristics, and the use of nanoparticles to improve the selectivity and sensitivity of devices. This review critically examines the cost-effectiveness, scalability, and practicality of point-of-use devices using graphene-based sensors and biosensors. This systematic review also discusses the potential risks to human health from antibiotic contamination and the role of monitoring in contributing to achieving the UN’s Sustainable Development Goals. This systematic review identifies a gap between developing sensors in laboratories versus their deployment as field-deployable devices; it highlights challenges associated with stability, matrix effects and the complexity of manufacturing devices. Finally, it provides recommendations for future research that may help to address this gap to promote the transition of innovative devices from academic to practical applications.

Introduction. triple negative breast cancer is an aggressive clinical phenotype characterized by poor prognosis. immune system plays an important role in the development, treatment response, and progression of solid tumor. The search for immune-related markers associated with the prediction of treatment efficacy and disease prognosis, and based on the use of high-resolution molecular techniques, is a promising area of research, the results of which can be translated into clinical practice. Case description. The molecular profile of blood mononuclear cells in a 48-year-old female patient with histologically proven triple negative breast cancer (estrogen Receptor – 0; progesteron Receptor – 0; Her2/neu – 0; gata-3 – 0, androgen Receptor – 0 and Ki67 – 70 %) was described. The patient did not response to neoadjuvant chemotherapy with 4 cycles of paclitaxel + carboplatin followed by 2 cycles of adriamycin + cyclophosphamide. The patient underwent surgery. disease progression (pelvic bone metastases) occurred 2 months after surgery. The features of blood lymphocytes and monocytes associated with a lack of response to neoadjuvant chemotherapy and disease progression were described.Conclusion. This clinical case demonstrates that sequencing of peripheral blood mononuclear cells can be used as a method for identifying predictive markers of therapy efficacy and developing personalized treatments for patients with triple negative breast cancer.

The integration of artificial intelligence (AI) and machine learning (ML) with electrochemical sensing has revolutionized analytical diagnostics by overcoming traditional limitations such as signal drift, peak overlapping, and matrix interference. However, despite the exponential growth of this field, a unified framework evaluating translational feasibility remains absent. This review critically analyzes AI/ML architectures applied to electrochemical sensors and biosensors from 2016 to 2025. To the best of our knowledge, this work introduces the first coded Network Evidence Map to quantitatively map the co-occurrence of methodological strengths, weaknesses, and translational barriers across the examined literature. The analysis reveals that while deep learning and ensemble models excel in signal deconvolution and multiplexing, the field is severely constrained by systemic bottlenecks. Network pathways demonstrate that over 83% of studies lack uncertainty quantification, and data scarcity coupled with restricted data-sharing policies critically undermines model reproducibility. Furthermore, batch-to-batch hardware variability measurably co-occurs with the opacity of black-box algorithms, hindering regulatory approval. We conclude that advancing from laboratory proof-of-concept to real-world point-of-care deployment necessitates a paradigm shift toward open-source electrochemical repositories, explainable AI (XAI), physics-informed machine learning, and hardware-software co-design.