Micro and nanomechanical resonators are powerful and label-free sensors of analytes in various environments. Their response, however, is a convolution of mass, rigidity, and nanoscale heterogeneity of adsorbates. Here we demonstrate a procedure to disentangle this complex sensor response, to simultaneously measure both mass and elastic properties of nanometer thick samples. This turns an apparent disadvantage of these resonators into a striking and unique asset, enabling them to measure more than mass alone.

Mechanical signaling involved in molecular interactions lies at the heart of materials science and biological systems, but the mechanisms involved are poorly understood. Here we use nanomechanical sensors and intact human cells to provide unique insights into the signaling pathways of connectivity networks, which deliver the ability to probe cells to produce biologically relevant, quantifiable and reproducible signals. We quantify the mechanical signals from malignant cancer cells, with 10 cells per ml in 1000-fold excess of non-neoplastic human epithelial cells. Moreover, we demonstrate that a direct link between cells and molecules creates a continuous connectivity which acts like a percolating network to propagate mechanical forces over both short and long length-scales. The findings provide mechanistic insights into how cancer cells interact with one another and with their microenvironments, enabling them to invade the surrounding tissues. Further, with this system it is possible to understand how cancer clusters are able to co-ordinate their migration through narrow blood capillaries.

Background. For more than 60 years, the synthetic opioid fentanyl has been widely used in anaesthesia and analgesia. While the intravenous formulation is primarily used for general anaesthesia and intensive care settings, the drug’s high lipophilic properties also allow various noninvasive routes of administration. Published data suggest that intranasal administration is also attractive for use as intranasal patient-controlled analgesia (PCA). A newly developed intranasal fentanyl formulation containing 47 μg fentanyl, intravenous fentanyl, and oral transmucosal fentanyl citrate were characterised, and bioavailability was compared to assess the suitability of the intranasal formulation for an intranasal PCA product. Methods. 27 healthy volunteers were enrolled in a single-centre, open-label, randomised (order of treatments), single-dose study in a three-period crossover design. The pharmacokinetics of one intranasal puff of fentanyl formulation (47 μg, 140 mL per puff), one short intravenous infusion of 50 μg fentanyl, and one lozenge with an integrated applicator (200 μg fentanyl) were studied, and bioavailability was calculated. Blood samples were collected over 12 hours, and plasma concentrations of fentanyl were determined by HPLC with MS/MS detection. Results. 24 volunteers completed the study. The geometric mean of AUC_0-tlast was the highest with oral transmucosal administration (1106 h pg/ml, CV% = 32.86), followed by intravenous (672 h pg/ml, CV% = 32.18) and intranasal administration (515 h pg/ml, CV% = 30.10). C_max was 886 pg/ml (CV% = 59.38) for intravenous, 338 pg/ml (CV% = 45.61) for intranasal, and 310 pg/ml (CV% = 29.58) for oral transmucosal administration. t_max was shortest for intravenous administration (0.06 h, SD = 0.056), followed by intranasal (0.21 h, SD = 0.078) and oral transmucosal administration (1.20 h, SD = 0.763). Dose-adjusted absolute bioavailability was determined to be 74.70% for the intranasal formulation and 41.25% for the oral transmucosal product. In total, 38 adverse events (AEs) occurred. Fourteen AEs were potentially related to the investigational items. No serious AE occurred. Conclusion. Pharmacokinetic parameters and bioavailability of the investigated intranasal fentanyl indicated suitability for its intended use as an intranasal PCA option.

The alarming increase of pathogenic bacteria that are resistant to multiple antibiotics is now recognized as a major health issue fuelling demand for new drugs. Bacterial resistance is often caused by molecular changes at the bacterial surface, which alter the nature of specific drug-target interactions. Here, we identify a novel mechanism by which drug-target interactions in resistant bacteria can be enhanced. We examined the surface forces generated by four antibiotics; vancomycin, ristomycin, chloroeremomycin and oritavancin against drug-susceptible and drug-resistant targets on a cantilever and demonstrated significant differences in mechanical response when drug-resistant targets are challenged with different antibiotics although no significant differences were observed when using susceptible targets. Remarkably, the binding affinity for oritavancin against drug-resistant targets (70 nM) was found to be 11,000 times stronger than for vancomycin (800 μM), a powerful antibiotic used as the last resort treatment for streptococcal and staphylococcal bacteria including methicillin-resistant Staphylococcus aureus (MRSA). Using an exactly solvable model, which takes into account the solvent and membrane effects, we demonstrate that drug-target interactions are strengthened by pronounced polyvalent interactions catalyzed by the surface itself. These findings further enhance our understanding of antibiotic mode of action and will enable development of more effective therapies.

The cantilever sensor, which acts as a transducer of reactions between model bacterial cell wall matrix immobilized on its surface and antibiotic drugs in solution, has shown considerable potential in biochemical sensing applications with unprecedented sensitivity and specificity1-5. The drug-target interactions generate surface stress, causing the cantilever to bend, and the signal can be analyzed optically when it is illuminated by a laser. The change in surface stress measured with nano-scale precision allows disruptions of the biomechanics of model bacterial cell wall targets to be tracked in real time. Despite offering considerable advantages, multiple cantilever sensor arrays have never been applied in quantifying drug-target binding interactions. Here, we report on the use of silicon multiple cantilever arrays coated with alkanethiol self-assembled monolayers mimicking bacterial cell wall matrix to quantitatively study antibiotic binding interactions. To understand the impact of vancomycin on the mechanics of bacterial cell wall structures1,6,7. We developed a new model1 which proposes that cantilever bending can be described by two independent factors; i) namely a chemical factor, which is given by a classical Langmuir adsorption isotherm, from which we calculate the thermodynamic equilibrium dissociation constant (Kd) and ii) a geometrical factor, essentially a measure of how bacterial peptide receptors are distributed on the cantilever surface. The surface distribution of peptide receptors (p) is used to investigate the dependence of geometry and ligand loading. It is shown that a threshold value of p ~10% is critical to sensing applications. Below which there is no detectable bending signal while above this value, the bending signal increases almost linearly, revealing that stress is a product of a local chemical binding factor and a geometrical factor combined by the mechanical connectivity of reacted regions and provides a new paradigm for design of powerful agents to combat superbug infections.

The correlation between circulating microbes and sepsis as well as proinflammatory diseases is increasingly gaining recognition. However, the detection of microbes’ cell-free DNA (cfDNA), which exist at concentrations of a billion times lower than blood proteins, poses a significant challenge for early disease detection. Here, we present Nano mechanics combined with highly sensitive readout sequences to address the challenges of ultralow counts of disease biomarkers, thus enabling robust quantitative monitoring of chronic medical conditions at different stages of human disease progression. To showcase the effectiveness of our approach, we employ fragments of cfDNA and human cell secretory proteins as models with predictive capabilities for human diseases. Notably, our method reveals a reliable representation over an impressive three to four orders of magnitude in the detection limit and dynamic range, surpassing commercially available quantitative polymerase chain reaction (qPCR) commonly used in routine clinical practice. This concept underpins a highly sensitive and selective medical device designed for the early detection of circulating microbes in patients undergoing intensive cancer therapy. This will help pinpoint individuals at risk of complications, including damage to the intestinal barrier and development of neutropenic fever/Sirsa/Sepsis. Moreover, this approach introduces new avenues for stratifying antibiotic prophylaxis in proinflammatory diseases.