ACS Sensors

Measuring the precise dynamics of specific neuromodulators in neurons is key to understanding how information is transmitted and processed in the brain. Despite the growing use of intensity-based neural activity sensors such as dLight in recent years, it remains challenging to monitor the dynamics of multiple neurochemicals simultaneously in live neurons. Unlike intensiometric sensors that are susceptible to artifacts caused by fluctuations in excitation power and rapid photobleaching, fluorescence lifetime-based sensors are less affected by these factors and can provide more stable measurements. Here we introduce dHaloLife635, a novel genetically encoded fluorescence-lifetime-based dopamine (DA) sensor that exhibits a fluorescence lifetime change up to -0.20 ns upon dopamine binding. dHaloLife635 consists of a G protein-coupled dopamine receptor (DRD1), a circularly permuted HaloTag (cpHaloTag), and a cell-permeable dye JF635 functionalized with a HaloTag ligand, a reactive chloroalkane linker (JF635-HTL). When dopamine binds to the dHaloLife635 sensor, it induces a conformational change in the cpHaloTag that alters the local environment of the conjugated dye, thereby changing its fluorescence lifetime. In live HEK293T cells and primary neurons, dHaloLife635 responds selectively to dopamine but not to serotonin, L-DOPA, or GABA, demonstrating excellent specificity. This dHaloLife635 sensor represents an initial step toward the establishment of a broader family of chemigenetic lifetime sensors that exhibit distinct fluorescence lifetime signatures upon binding their respective targets, thereby enabling highly specific and sensitive, real-time monitoring of multiple neurochemical dynamics in vivo.

Extracellular L-lactate reports cellular metabolism, but resolving its dynamics alongside intracellular metabolism and neuronal activity remains challenging. Here we develop eLACCO3 and R-eLACCO3, green and red cell-surface lactate biosensors with Ca2+-independent responses and faster response and recovery kinetics in cultured cells. Selected variants provide larger fluorescence responses than their predecessors in brain cells, slices, and living mice and improve detection of glucose-evoked extracellular lactate oscillations. Combining R-eLACCO3 with GCaMP6f enabled imaging of extracellular L-lactate simultaneously with odor-evoked activity in anesthetized mice and cortical activity in awake mice. These biosensors enable multiplexed measurements of extracellular lactate dynamics in relation to intracellular metabolism and neuronal activity.

Extracellular signal-regulated kinase (ERK) governs many cellular processes, and its spatiotemporal activity dynamics are important for downstream signaling specificity. Existing fluorescent ERK activity biosensors, however, fall short in enabling spectrally multiplexed, spatially resolved, or deep tissue measurements. Here, we introduce a chemigenetic, single-fluorophore biosensor, WHaloEKAR, that reports ERK activities through changes in fluorescence lifetime. WHaloEKAR combines large lifetime changes (-0.81 ns) with the spectral versatility of the self-labeling protein HaloTag giving access to orange, red, and far-red ERK biosensors. The latter enabled the investigation of heterogeneous ERK activity at distinct subcellular locations in cultured cell lines and primary cortical rat neurons via fluorescence lifetime imaging microscopy (FLIM). Moreover, multiplexed FLIM measurements allowed to study ERK activity in parallel to the second messengers calcium and cyclic adenosine monophosphate. Finally, WHaloEKAR enabled the investigation of ERK activity in 3D samples as well as in live animals.

Organic electrochemical transistors (OECTs) enable high transconductance at low operating voltages through the volumetric charging of organic mixed ionic/electronic conductors (OMIECs), making them attractive transducers for biosensing. However, the same ionic -electronic coupling makes OMIECs, such as poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS), sensitive to different ions and various environmental fluctuations, so drift and memory effects may compromise the quantitative readout in complex biofluids. In this work, we introduce a novel OECT-based sensor configuration that minimises drift by addressing a single channel with two gates: a functionalised sensing gate and a reference gate that allows real-time decoupling of environmental and channel-state fluctuations. A pulsed gate-biasing protocol is introduced to promote channel conductivity recovery and mitigate state accumulation. Using enzymatic glucose sensing as a proof of concept, the platform suppresses time-dependent drift by ~96%, eliminates temperature-induced variations up to 99% across the 20-70 {degrees}C range, improves calibrated sensitivity by 55% and reduces interferent-induced deviations under physiologically relevant conditions. By improving both signal stability and analytical reliability, this strategy addresses a central limitation of OECT biosensors and advances their use in high-sensitivity point-of-care biosensing in complex biological environments.

Protein-based fluorescent sensors are a powerful addition to the biology toolbox for their ability to be stably expressed within living organisms, tissues, cells, and subcellular compartments, with the capacity to report on the presence of specific target molecules or other analytes. At the same time, sensor components will unavoidably present opportunities for unintended interaction with endogenous cellular machinery, potentially confounding both sensor function and cell health. Interactions with host components may not be readily predictable during the sensor design process, especially when simultaneously optimizing many other sensor parameters such as fluorescence response, dynamic range, and kinetics. Characterizing effects of sensor expression on cells is currently a laborious ad hoc process; new methods to characterize the cell expression effects of sensors and their variants could dramatically improve sensor design pipelines, laying the groundwork to recognize potentially problematic expression side effects earlier in the iterative design and testing workflow. Here, we take a dual high-content imaging-based and biochemical approach to examine sensor interactions with native cell biology, focusing on the widely used GCaMP calcium sensor. We identify a morphology-based signature of the cellular effects of high sensor expression in a neuroblastoma cell line. Subsequently, we identify biochemical interactions between GCaMP and a component of the mammalian cytoskeleton and track morphological features in sensor-expressing cells that lack these structural components. Our findings present an entry point for engineering new minimally cross-reactive sensor versions given a contextual biological understanding of sensor overexpression. We anticipate that as this and related workflows are incorporated into sensor engineering pipelines, bioorthogonality can be more systematically assessed and prioritized in diverse sensor scaffolds.

Methotrexate (MTX) is a narrow-therapeutic-index drug widely used in cancer and autoimmune disease treatment, for which delayed clearance can cause severe toxicity and requires close therapeutic drug monitoring. Here, we integrate high-stringency Capture-SELEX, biophysical characterization, molecular modeling, mutational validation, and electrochemical transduction to develop two structurally distinct MTX-binding DNA aptamer families. Full-length 3315 and 930 bound MTX with apparent KD values of 89 and 408 nM, respectively. Systematic truncation generated compact functional variants: 3315-55 retained 63 nM affinity and broad buffer tolerance, whereas 930-65 reached an apparent KD of 44 nM in 10 mM Mg2, revealing markedly different ion dependencies. Biolayer interferometry confirmed rapid and reversible MTX recognition, while 3315 variants retained binding in serum-containing buffer. Computational analysis and site-directed mutagenesis identified distinct recognition architectures: a compact G-rich scaffold coupled to a central binding interface in 3315 and a localized Mg2+-stabilized binding pocket in 930. Methylene blue-labeled 3315-55 and 930-65 were integrated into electrochemical aptamer-based sensors, producing concentration-dependent responses to 100-1000 uM MTX in PBS, discriminating MTX from folic acid, and responding to MTX directly in undiluted human blood. Together, these results establish a structure-guided workflow linking aptamer discovery, mechanistic validation, and electrochemical translation, providing new molecular recognition elements and a foundation for future reagent-less MTX therapeutic drug monitoring.

Hormone dynamics are central to a multitude of conditions in women's health; however, limitations in the cost, convenience, and multiplexing capacity of current hormone assays preclude routine longitudinal hormone testing. This limitation severely restricts our understanding in this historically neglected field, presenting a need for improved bioanalytical tools to support hormone-informed care. Silicon photonic resonator sensors have excellent multiplexing capacity and scalable fabrication, and offer strong potential to meet this important need. Here we demonstrate, for the first time, multiplexed silicon photonic detection of two urinary hormone biomarkers, follicle-stimulating hormone (FSH) and pregnanediol-3-glucuronide (PdG), using sub-wavelength grating ring resonators and a polydopamine-based, regenerable surface chemistry. We combine a sandwich immunoassay for FSH and an antigen-down competitive immunoassay for PdG on the same chip, both amplified using a commercially available quantum-dot-streptavidin conjugate and with on-chip regeneration over multiple measurement cycles. In artificial urine, this proof-of-principle multiplexed assay achieves limits of detection of 0.594 ng/mL for FSH and 19.1 ng/mL for PdG. This architecture is inherently scalable to tens of hormone targets on a millimetre-scale chip (a >10x multiplexing improvement over typical lateral-flow immunoassays) enabling future low-cost, quantitative at-home hormone panels that could transform research, diagnostics, and treatment in women's health.

Continuous biomarker monitoring with affinity biosensors remains limited by probe saturation, slow dissociation, signal carryover, and regeneration methods that require chemical washing, harsh buffers, or external reset modules. Here, we report a flow-programmed, self-regenerative electrochemical aptamer-based biosensor for continuous 17{beta}-estradiol monitoring. The platform restores the sensing interface by tuning buffer flow through the device, enabling repeated sensing-regeneration cycles without chemical stripping or active-reset hardware. The biosensor achieves a 1.46 pM limit of detection, and a log-linear dynamic range from 100 pM to 1 M in real human sweat, covering physiologically relevant estradiol concentrations. It exhibits high selectivity, with responses to potential molecularly similar interferents remaining below 10% of the 17{beta}-estradiol signal. Under dynamic flow operation, the device shows reversible signal recovery and stable electrochemical performance, with higher buffer-wash flow reducing baseline carryover between measurements. A coupled binding-transport simulation captures the flow-dependent regeneration trends and maps how wash-flow rate, wash duration, and apparent aptamer affinity affect regeneration efficiency. Ex vivo testing in real human sweat, including samples from pregnant women, supports translational relevance. This work establishes buffer-flow programming as a reagent-free route to regenerable electrochemical aptamer sensing for automated, wearable-compatible estradiol monitoring.

Unlike nucleic acids, proteins cannot be directly amplified. Ultrasensitive detection must therefore couple efficient analyte-to-signal conversion with strong suppression of target-independent background. Consistently detecting ~100 molecules (below which Poisson sampling noise alone causes CV to exceed 10%) across protein targets in a format as scalable and accessible as PCR remains unsolved. Separation-based ultrasensitive immunoassays require target binding stable enough to survive stringent washing and are therefore constrained by wash-induced signal loss and, more fundamentally, the relative scarcity of exceptionally tight binders. Performance consequently varies across targets; detection limits below 1,000 molecules remain rare. Conventional separation-free proximity assays avoid washing but incur background from random probe collisions, with reported detection limits frequently exceeding 100,000 molecules. Here we introduce Successive Proximity Extension Amplification Reaction (SPEAR), a separation-free architecture in which autonomous engineered DNA nanodevices emulate biological kinetic proofreading. Two successive irreversible proximity-extension steps compound discrimination between analyte-dependent and background reactions, suppressing spurious signals while preserving efficient analyte-to-signal conversion. This design reduces reliance on high-affinity binders and enables sub-fM detection from 1 uL samples through simple 3-step mixing followed by standard qPCR readout. Across 11 biomarkers tested, SPEAR consistently achieved sub-100-molecule detection limits. Where compared, it outperformed ultrasensitive separation-based platforms, analytically and clinically, including for low-abundance Alzheimer's disease biomarkers. SPEAR also precisely quantified SARS-CoV-2 neutralizing antibody titers from finger-prick dried blood spots, concordant with the cell-based reference assay on venous blood. It detected a distinct mixed phosphorylation state of Tau, and targeting this state resolved previously observed capillary-venous discordance and improved amyloid-status discrimination. These results establish engineered autonomous successive molecular discrimination as a practical strategy for near-single-molecule protein detection, extending the sensitivity, scalability and accessibility of PCR-based testing to non-amplifiable analytes.

Immunoassays are critical for clinical diagnostics, yet their performance is restricted by the physical constraints of flat surfaces used to immobilize capture molecules which limit loading capacity and induce protein denaturation. Hydrogels provide immense volumetric molecular loading capacity but translating them across diverse assay formats and readout modalities without compromising analyte permeability remains challenging. Here, we present a universal strategy for hydrogel integration into immunoassay platforms for enhanced optical and electronic biosensing. Utilizing bio-orthogonal photo-click chemistry, we developed a modular and tuneable poly(ethylene glycol) (PEG) matrix that achieves a 6,000-fold increase in molecular loading and a large mesh size for analyte diffusion. This 3D architecture demonstrates broad multimodal utility with easy integration into a range of binding-based assays enabling higher sensitivity, repeatability and multiplexability. It achieves high sensitivity enhancement for fluorescence detection in microarrays (38-fold) and microtiter plates (25-fold) and enables multiplexed readout in single wells, including from clinical serum samples. In silver metallization-based assays it boosts densitometric optical detection sensitivity (18-fold) and enables inexpensive electronic detection. In lateral flow assays, it generates 20-fold signal enhancement while enabling multiplexability and higher repeatability. By standardizing high-capacity volumetric biosensing, this scalable, plug-and-play technology empowers next generation highly sensitive, field-deployable point-of-care diagnostics.

Detection of molecules with cell-based sensors allows for conversion of binding events into gene expression outputs. Here, we present a cell-based sensor that can detect extracellular double-stranded DNA. This sensor is based on an engineered receptor which we call Luminescent Ultrasensitive Nucleic Acid Reporter, or LUNAR. LUNAR is based on a recently developed Programmable Antigen-gated G-protein-coupled Engineered Receptor (PAGER). PAGERs are a genetic fusion of an auto-inhibitory peptide, a protein-binding domain, and a modified kappa opioid receptor. PAGERs are gated by two binding events. First, a protein ligand displaces an intramolecular inhibitor, Arodyn, then a second ligand activates the receptor. By replacing the protein-binding domain with a DNA binding zinc finger protein (ZFP) we could detect extracellular DNA in a dose-dependent fashion. Here, we show that first-generation LUNAR constructs can detect both oligonucleotides and plasmid double-stranded DNA with nanomolar sensitivity in mammalian cells. Future work will focus on improving sensitivity, fold-change, and multiplexing capabilities for sequence-specific DNA detection.

Monitoring pH and extracellular acidification rate (ECA) in biological samples containing live mammalian cells can provide valuable information on the glycolytic activity and bioenergetic status of cells. Compared to pH electrodes, optochemical pH sensors look more advantageous, since they allow rapid, non-invasive parallel analysis of multiple samples with stable readout of pH. We have developed new fluorescent pH sensors based on hydrophobic protonable metal-free porphyrins,OEP and OEPK, embedded in a plasticized PVC matrix containing a proton transfer agent. These pH sensors provide internally-referenced calibration-free operation, both in ratiometric intensity and lifetime-based detection modes. Sensor development included optimization of the indicator dye and its photophysical characteristics, screening of different proton transfer agents to minimize sensor toxicity, tuning of the protonation range and pKa, long-term storage stability and response time studies. Optimised pH sensor coatings were then deposited on plastic substrates (96-well microplates) and used for real-time monitoring of Extracellular Acidification Rate (ECAR) for cultured cancer cells and 3D spheroid structures on standard laboratory equipment (multi-label plate reader and confocal FLIM microscope). The advanced pH sensors tailored for use with biological samples have high potential for cell analysis and related applications.

Forster resonance energy transfer (FRET) is commonly used to monitor protein-protein interactions in situ. The high spatiotemporal resolution and facile implementation inside complex molecular environments have spearheaded FRET's widespread adoption in biosensing. Despite these advantages, current FRET biosensors are largely restricted to the detection of the presence/absence of individual inputs and are thus unable to sense several multiplexable inputs simultaneously within complex milieu of biological environments. In this work, we introduce a generalizable strategy to construct genetically encoded protein-based FRET biosensors capable of recognizing multiple inputs following Boolean logic-type (YES/OR/AND) operations. These topologically specified FRET sensors powerfully expand the input capacity in sensing protein-protein interactions while providing a user-programmable platform for monitoring heterogeneous biological activities both in vitro and in living cells.

Solid-state nanopores offer a versatile platform for single-molecule sensing owing to their mechanical robustness, tuneable geometry, and compatibility with scalable fabrication. Here, we present a pyramidal silicon nanopore with a 40 nm sensing aperture for label-free characterisation of protein molecules by resistive pulse sensing. The nanopore operates stably over transmembrane voltages ranging from -2 to +2 V and across a broad range of electrolyte concentrations, enabling analysis under diverse experimental conditions. As molecules traverse the confined sensing region, transient ionic current modulations are generated that reflect their excluded volume and molecular geometry. Using this approach, we characterise unlabelled Tau species spanning monomeric proteins, intermediate aggregates, and mature fibrillar assemblies. Analysis of the resulting current signatures, together with simplified geometrical models, enables reconstruction of molecular dimensions and discrimination of distinct Tau populations based on their electrical fingerprints. These results demonstrate that pyramidal silicon nanopores provide a sensitive and scalable platform for label-free monitoring of structurally heterogeneous protein aggregation and establish a framework for investigating protein aggregation using solid-state nanopore sensing.

Perovskite quantum dots (PQDs) are exceptionally promising next-generation optical materials for bioimaging applications. This paper presents an optical PQDs nanocomposite method to identify bacterial species associated with breast cancer, employing a LSTM deep learning model. The results indicate that the developed PQD-based nano-sensor offers impressive sensitivity, selectivity, and practical application. The four bacterial species identified in the study, Pseudomonas aeruginosa, Vibrio parvula, Acinetobacter baumannii, and Streptococcus vestibularis, were considered the most relevant indicators of breast cancer. With rapid detection, an intuitive design, and improved reliability, the PQD sensor could be an excellent breast cancer diagnostic tool for a point-of-care application. Furthermore, it has the potential to be integrated into healthcare systems, specifically in resource-limited settings, thereby improving the accessibility and efficiency of early breast cancer detection.

Electrochemical aptamer-based (EAB) sensors have emerged as a promising framework for reagentless, real-time molecular monitoring in complex biological matrices. Signal transduction in a prominent benchmark system--targeting aminoglycoside antibiotics--has been nearly universally attributed to target-specific, binding-induced conformational changes that modulate the distance between a redox reporter and the electrode surface. Here, using a suite of interfacial electrochemical and optical characterization methods, we systematically show that charge-screening, not structure switching, dominates the signaling mechanism in the case of the aminoglycoside EAB sensor. First, we demonstrate that the legacy aminoglycoside sensor sequence exhibits apparent affinities and relative electron kinetic modulations similar to unrelated control sequences, including generic stem-loop hairpins, unstructured poly-T sequences, and short duplexes. Multi-modal validation via bio-layer interferometry (BLI), circular dichroism (CD), and fluorescence spectroscopy confirms that this promiscuous target association occurs across disparate DNA sequences and without detectable conformational reconfiguration. Further investigation via charge-scaling polyamine assays and on-electrode fluorescence imaging demonstrate that the dominant signal transduction mechanism in these sensors is instead driven by macro-scale electrostatic backbone charge-screening.

Non-small-cell lung cancer (NSCLC) diagnosis is challenged owing to the need for reliable, non-invasive biomarkers. Circulating cell-free microRNAs (ccf-miRNAs) are promising candidates, but their low levels and short sequences in blood complicate detection. In this study, a novel nano-hybrid fluorescent array was developed to enable rapid and sensitive identification of NSCLC-related ccf-miRNAs. The array consists of poly(T)-modified fluorescent nano-polystyrene beads for capturing target miRNAs, and fluorophore-labeled, sequence-specific locked nucleic acid (LNA) probes for their detection. This design forms a sandwich structure with the target miRNA. Nano cytometry and fluorescence microscopy assessed both the capture and specificity of detection. The assay was tested on plasma samples for miR-16-5p and U6, evaluating selectivity, sensitivity, and reproducibility. The results showed that the poly(T)-modified beads efficiently captured the target miRNAs, and the LNA probes accurately distinguished the sequences. The assay enabled direct detection of miR-16-5p and U6 from plasma without amplification, demonstrating high selectivity, sensitivity, and reproducibility. Combining enrichment with nano-polystyrene beads and sequence-specific LNA probes addressed the main challenges of ccf-miRNA detection, namely their low abundance and short sequence length. As ccf-miRNAs are linked to NSCLC progression, this method could assist in early diagnosis, disease monitoring, and assessment of treatment response. The target-independent nature of the poly(T) capture layer allows for easy calibration to new miRNAs by changing the LNA probes, rendering it suitable for multiplex detection in clinical settings. However, further preclinical and clinical studies are needed before adoption in routine practice. This nano-hybrid fluorescent array presents a rapid and reliable approach for detecting low-abundance ccf-miRNAs in plasma. Its encouraging performance and flexible design show potential for future application in NSCLC diagnosis and monitoring, subject to additional confirmation.

Continuous tracking of cortisol is central to understanding human stress physiology, yet direct electrochemical detection without biological receptors remains challenging despite its promise for stable and dynamic sensing. Electrochemical reduction of cortisol typically occurs at highly negative potentials, where parasitic interfacial currents, hydrogen evolution, and substantial capacitive background overlap with the cortisol reduction signal, preventing accurate quantification. We overcome this barrier through an integrated material-measurement strategy combining a defect-engineered graphene interface with a transition-resolved interrogation (TRI) measurement strategy. First, we engineered polybenzimidazole-derived laser-induced graphene containing nitrogen-rich defects while suppressing oxygen-derived functionalities which reduced parasitic background currents within the same cathodic potential regime. Next, we designed TRI to leverage differences in the time-dependent evolution of overlapping cathodic processes to isolate a localized cortisol-associated electrochemical reduction transition. Derivative-domain projection coupled with background estimation enables its reliable quantification. This integrated sensing architecture enables sensitive, selective, and dynamic cortisol detection in both artificial and biological interstitial fluids at low nanomolar concentrations. The response remains reproducible across physiologically relevant variations in pH, ionic strength, temperature, and repeated cycling. Together, these capabilities provide a basis for continuous electrochemical cortisol monitoring for future study of stress physiology.

Continuous monitoring of proteins in complex biological fluids is essential for advancing personalized medicine, yet existing biosensors are often limited by instability, single-use designs, and insufficient sensitivity. Here, we describe a detailed protocol for the fabrication and operation of molecular pendulum (MP) electrochemical sensors integrated with an active-reset mechanism to enable real-time, reversible, and ultrasensitive protein detection. The protocol is described in two parts. First, we describe the microfabrication of gold microelectrodes and their nanostructured modification, followed by assembly of DNA-based pendulum probes with redox reporters and affinity receptors that translate binding into kinetic electron-transfer shifts. Second, we detail the sensing and active-reset approach, which detects the target analyte and applies tunable oscillatory potentials to accelerate its dissociation, regenerate sensor surfaces, and extend operational lifetime. The protocol includes detailed guidance on device fabrication, surface functionalization, sensing and reset cycles, and data analysis. When implemented, MP sensors with active-reset achieve pg/ml sensitivity, rapid equilibration, and robust performance across biofluids and in situ models, enabling continuous protein monitoring over extended periods. This combined technology represents a biosensing platform with significant potential, opening new avenues for wearable and implantable molecular monitoring, early disease detection, and personalized therapeutic guidance.

BackgroundContinuous glucose monitoring (CGM) systems are used not only for retrospective assessment of the glycemic profile but also for real-time decision-making, including automated insulin delivery. Accordingly, CGM performance characterization must capture not only the agreement of individual paired values but also the systems ability to reproduce the direction, rate, amplitude, and shape of glucose concentration change. Summary metrics, most notably MARD, cannot establish whether an observed deviation reflects an error in the formation of the test profile itself, a constant sensor offset, amplitude compression, a change in response rate, temporal misalignment, or hysteresis. ObjectiveTo adapt a programmable flow-based in vitro platform for the separate assessment of the experimentally delivered glucose profile and the dynamic response of CGM systems, and to propose a set of metrics that decomposes dynamic error into its components. MethodsGLU profiles were generated by programmable mixing of solutions at a constant total flow rate of 2 mL/min. Actual GLU concentration was independently measured with a SUPER GL2 glucose analyzer. Four static levels, three repeats of a 5.5[->]12.0[->]5.5 mmol/L profile, three repeats of a 6.0[->]3.0[->]6.0 mmol/L hypoglycemic profile, three 5.0[->]15.0[->]5.0 mmol/L profiles at different rates, one complex 4[->]18[->]3[->]12[->]5.5 mmol/L profile, and two proof-of-concept sensor experiments at 100- and 200-min transitions were investigated. Dynamic response was characterized by bias, MAE, RMSE, MARD, amplitude transfer coefficient K_A, rate transfer coefficients K_up and K_down, normalized shape RMSE, residual shift, and hysteresis loop area. ResultsAt the static levels, measured GLU exceeded the programmed value by 0.234-0.780 mmol/L. In the repeated 5.5[->]12.0[->]5.5 profiles, the ratio of actual to programmed rate was 0.978-1.083 on the rising phase and 0.987-1.157 on the falling phase, while the amplitude transfer coefficient was 0.967-1.066. In the hypoglycemic profile, minimum GLU was 2.55- 2.96 mmol/L, and time below 3.0 mmol/L was 15.2-72.6 min. The measured rates of 0.0519, 0.1045, and 0.2027 mmol/L/min preserved the intended ratio of approximately 1:2:4. In the complex profile, the programmed plateau of 18 mmol/L was not reached: mean measured GLU was 16.20 mmol/L. For CGM-A, K_A was 0.682 and 0.650, and K_up/K_down were 0.666/0.730 and 0.634/0.626; the corresponding values for CGM-B were 1.228 and 1.128, and 1.564/1.328 and 1.276/1.145. Hysteresis loop area differed 5- to 10-fold between the two sensor responses, exceeding an order of magnitude at the 100-min transition. ConclusionThe programmed concentration should be treated as a control setpoint, rather than as a reference measurement. The "programmed trajectory -- measured glucose -- CGM output" cascade first allows quantitative assessment of the agreement between the programmed and actually realized profile and only then separate characterization of sensor response. Decomposition of dynamic error into amplitude, rate, shape, and hysteresis components reveals differences that a single MARD value or correlation coefficient cannot capture.