Integrating Photoactive Ligands into Crystalline Ultrathin 2D Metal-Organic Framework Nanosheets for Efficient Photoinduced Energy Transfer.

3D metal-organic frameworks (MOFs) have gained attention as heterogeneous photocatalysts due to their porosity and unique host-guest interactions. Despite their potential, MOFs face challenges, such as inefficient mass transport and limited light penetration in photoinduced energy transfer processes. Recent advancements in organic photocatalysis have uncovered a variety of photoactive cores, while their heterogenization remains an underexplored area with great potential to build MOFs. This gap is bridged by incorporating photoactive cores into 2D MOF nanosheets, a process that merges the realms of small-molecule photochemistry and MOF chemistry. This approach results in recyclable heterogeneous photocatalysts that exhibit an improved mass transfer efficiency. This research demonstrates a bottom-up synthetic method for embedding photoactive cores into 2D MOF nanosheets, successfully producing variants such as PCN-641-NS, PCN-643-NS, and PCN-644-NS. The synthetic conditions were systematically studied to optimize the crystallinity and morphology of these 2D MOF nanosheets. Enhanced host-guest interactions in these 2D structures were confirmed through various techniques, particularly solid-state NMR studies. Additionally, the efficiency of photoinduced energy transfer in these nanosheets was evidenced through photoborylation reactions and the generation of reactive oxygen species (ROS).

Deep-Learning-Based Nanomechanical Vibration for Rapid and Label-Free Assay of Epithelial Mesenchymal Transition.

Cancer is a profound danger to our life and health. The classification and related studies of epithelial and mesenchymal phenotypes of cancer cells are key scientific questions in cancer research. Here, we investigated cancer cell colonies from a mechanical perspective and developed an assay for classifying epithelial/mesenchymal cancer cell colonies using the biomechanical fingerprint in the form of "nanovibration" in combination with deep learning. The classification method requires only 1 s of vibration data and has a classification accuracy of nearly 92.5%. The method has also been validated for the screening of anticancer drugs. Compared with traditional methods, the method has the advantages of being nondestructive, label-free, and highly sensitive. Furthermore, we proposed a perspective that subcellular structure influences the amplitude and spectrum of nanovibrations and demonstrated it using experiments and numerical simulation. These findings allow internal changes in the cell colony to be manifested by nanovibrations. This work provides a perspective and an ancillary method for cancer cell phenotype diagnosis and promotes the study of biomechanical mechanisms of cancer progression.

HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.

Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.

Numerical Analysis of Linearly Implicit Methods for Discontinuous Nonlinear Gurtin-MacCamy Model.

In this study, we study a nonlinear age-structured population models with discontinues mortality and fertility rates, motivated by the fact that different maturation period may cause the significant difference in rates. We develop a novel numerical method with two-layer boundary conditions, the linearly implicit θ-methods on a special mesh. With a uniform boundedness analysis of numerical solutions, the finite time convergence is proved piecewisely according to the fundamental approach for the smooth rates. For juvenile-adult models, the existence of numerical endemic equilibrium is determined by a numerical basic reproduction function, which converges to the exact one with accuracy of order 1. Moreover, it is shown that for juvenile-adult models, the global stability of the disease-free equilibrium and the local stability of the endemic equilibrium are approximately exhibited by the numerical processes. Finally, some numerical experiments on the Logistic models and tadpoles-frogs models illustrate the verification and the efficiency of our results.

Nanomechanical assay for ultrasensitive and rapid detection of SARS-CoV-2 based on peptide nucleic acid.

The massive global spread of the COVID-19 pandemic makes the development of more effective and easily popularized assays critical. Here, we developed an ultrasensitive nanomechanical method based on microcantilever array and peptide nucleic acid (PNA) for the detection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) RNA. The method has an extremely low detection limit of 0.1 fM (105 copies/mL) for N-gene specific sequence (20 bp). Interestingly, it was further found that the detection limit of N gene (pharyngeal swab sample) was even lower, reaching 50 copies/mL. The large size of the N gene dramatically enhances the sensitivity of the nanomechanical sensor by up to three orders of magnitude. The detection limit of this amplification-free assay method is an order of magnitude lower than RT-PCR (500 copies/mL) that requires amplification. The non-specific signal in the assay is eliminated by the in-situ comparison of the array, reducing the false-positive misdiagnosis rate. The method is amplification-free and label-free, allowing for accurate diagnosis within 1 h. The strong specificity and ultra-sensitivity allow single base mutations in viruses to be distinguished even at very low concentrations. Also, the method remains sensitive to fM magnitude lung cancer marker (miRNA-155). Therefore, this ultrasensitive, amplification-free and inexpensive assay is expected to be used for the early diagnosis of COVID-19 patients and to be extended as a broad detection tool. Electronic Supplementary Material: Supplementary material (experimental section, N gene sequences and all nucleic acid sequences used in the study, Figs. S1-S6, and Tables S1-S3) is available in the online version of this article at 10.1007/s12274-022-4333-3.

Relay-type sensing mode: A strategy to push the limit on nanomechanical sensor sensitivity based on the magneto lever.

Ultrasensitive molecular detection and quantization are crucial for many applications including clinical diagnostics, functional proteomics, and drug discovery; however, conventional biochemical sensors cannot satisfy the stringent requirements, and this has resulted in a long-standing dilemma regarding sensitivity improvement. To this end, we have developed an ultrasensitive relay-type nanomechanical sensor based on a magneto lever. By establishing the link between very weak molecular interaction and five orders of magnitude larger magnetic force, analytes at ultratrace level can produce a clearly observable mechanical response. Initially, proof-of-concept studies showed an improved detection limit up to five orders of magnitude when employing the magneto lever, as compared with direct detection using probe alone. In this study, we subsequently demonstrated that the relay-type sensing mode was universal in application ranging from micromolecule to macromolecule detection, which can be easily extended to detect enzymes, DNA, proteins, cells, viruses, bacteria, chemicals, etc. Importantly, we found that, sensitivity was no longer subject to probe affinity when the magneto lever was sufficiently high, theoretically, even reaching single-molecule resolution. Electronic Supplementary Material: Supplementary material (experimental section) is available in the online version of this article at 10.1007/s12274-022-5049-0.

Magneto-Nanomechanical Array Biosensor for Ultrasensitive Detection of Oncogenic Exosomes for Early Diagnosis of Cancers.

Exosomes are a class of nanoscale vesicles secreted by cells, which contain abundant information closely related to parental cells. The ultrasensitive detection of cancer-derived exosomes is highly significant for early non-invasive diagnosis of cancer. Here, an ultrasensitive nanomechanical sensor is reported, which uses a magnetic-driven microcantilever array to selectively detect oncogenic exosomes. A magnetic force, which can produce a far greater deflection of microcantilever than that produced by the intermolecular interaction force even with very low concentrations of target substances, is introduced. This method reduced the detection limit to less than 10 exosomes mL-1 . Direct detection of exosomes in the serum of patients with breast cancer and in healthy people showed a significant difference. This work improved the sensitivity by five orders of magnitude as compared to that of traditional nanomechanical sensing based on surface stress mode. This method can be applied parallelly for highly sensitive detection of other microorganisms (such as bacteria and viruses) by using different probe molecules, which can provide a supersensitive detection approach for cancer diagnosis, food safety, and SARS-CoV-2 infection.

Personalised depression forecasting using mobile sensor data and ecological momentary assessment.

Introduction: Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising. Methods: We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention ( N = 65 patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models' ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day. Results: In our experiments, we achieve a best mean-absolute-error (MAE) of 0.801 (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline ( MAE = 1.062 ). For one day ahead-forecasting, we can improve the baseline of 1.539 by 12 % to a MAE of 1.349 using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level. Discussion: Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression.

Determination of 12 anti-obesity drugs in human plasma by a 96-well protein precipitation plate using HPLC-MS.

An analytical method was developed and validated for the simultaneous determination of 12 anti-obesity drugs (methylephedrine (MER), amphetamine (AMP), fenfluramine (FEN), bupropion (BUP), fluoxetine (FLU), sibutramine (SIBU), bisacodyl (BISA), bumetanide (BUM), lovastatin (LOVA), simvastatin (SIM), rimonabant (RIMO), and fenofibrate (FENO)) in human plasma by a 96-well protein precipitation plate combined with high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS). The 96-well protein precipitation plate was chosen for simultaneous pretreatment of large sample volumes, making the whole process more efficient and faster. Drugs were separated on an Agilent Poroshell 120 EC-C18 column, and detected by MS/MS under multiple reaction monitoring (MRM) mode. The developed method was validated in terms of linearity, matrix effect, accuracy and precision. A good linearity was obtained in the range of 0.1-20.0 ng mL-1 for fenfluramine, bupropion, fluoxetine, sibutramine, bisacodyl, and rimonabant; and 0.5-20.0 ng mL-1 for methylephedrine, amphetamine, bumetanide, lovastatin, simvastatin, and fenofibrate with a correlation coefficient above 0.995. The method was fully validated with an acceptable accuracy of 75.63-108.21%, matrix effect of 80.41-117.71% except for fenofibrate (76.07% at low concentration levels), and precision of 0.32-13.12%. Owing to the advantages of simple operation, high accuracy and sensitivity, this method is suitable for the rapid and simultaneous detection of 12 anti-obesity drugs in human plasma, providing support for clinically monitoring the development of adverse reactions and guiding the rational and appropriate use of weight-loss drugs for obese people.

Assembling Phenothiazine into a Porous Coordination Cage to Improve Its Photocatalytic Efficiency for Organic Transformations.

Photo-catalysis by small-molecules is often limited by catalyst degradation and low electron-transfer efficiency. Herein we report a stable N-phenyl-phenothiazine (PTH)-derived porous coordination cage (PCC) as a highly efficient photocatalyst. By the incorporation of the photocatalytic PTH moiety into a PCC, aggregation-induced quenching (AIQ) was shown to be reduced. An improvement in catalyst stability was discovered, ascribed to the synergistic effects of the PTH moieties. The catalyst, operating through a photolytic single-electron transfer, was utilized for photo-catalyzed dehalogenation and borylation. Evaluation of the catalytic mechanism in the borylation reaction showed that the improved performance results from the more efficient formation of the electron donor-acceptor (EDA) complex with the cage. This discovery provides a potential strategy to improve the photophysical properties and stabilities of small-molecule organic photocatalysts via supramolecular chemistry.

Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting.

Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the results of self-assessed Patient-Health-Questionnaires (PHQ), indicating current symptom severity of depressive individuals. Many of the currently available approaches to predict PHQ scores use passive data, e.g., from smartphones. However, there are several other scores and data besides PHQ, e.g., the Behavioral Activation for Depression Scale-Short Form (BADSSF), the Center for Epidemiologic Studies Depression Scale (CESD), or the Personality Dynamics Diary (PDD), all of which can be effortlessly collected on a daily basis. In this work, we explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12, using leave-one-subject-out cross-validation, as well as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the last 7 days. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.

Reducing Compaction Temperature of Asphalt Mixtures by GNP Modification and Aggregate Packing Optimization.

Compaction of hot mix asphalt (HMA) requires high temperatures in the range of 125 to 145 °C to ensure the fluidity of asphalt binder and, therefore, the workability of asphalt mixtures. The high temperatures are associated with high energy consumption, and higher NOx emissions, and can also accelerate the aging of asphalt binders. In previous research, the authors have developed two approaches for improving the compactability of asphalt mixtures: (1) addition of Graphite Nanoplatelets (GNPs), and (2) optimizing aggregate packing. This research explores the effects of these two approaches, and the combination of them, on reducing compaction temperatures while the production temperature is kept at the traditional levels. A reduction in compaction temperatures is desired for prolonging the paving window, extending the hauling distance, reducing the energy consumption for reheating, and for reducing the number of repairs and their negative environmental and safety effects, by improving the durability of the mixtures. A Superpave asphalt mixture was chosen as the control mixture. Three modified mixtures were designed, respectively, by (1) adding 6% GNP by the weight of binder, (2) optimizing aggregate packing, and (3) combining the two previous approaches. Gyratory compaction tests were performed on the four mixtures at two compaction temperatures: 135 °C (the compaction temperature of the control mixture) and 95 °C. A method was proposed based on the gyratory compaction to estimate the compaction temperature of the mixtures. The results show that all the three methods increase the compactability of mixtures and thus significantly reduce the compaction temperatures. Method 3 (combining GNP modification and aggregate packing optimization) has the most significant effect, followed by method 1 (GNP modification), and method 2 (aggregate packing optimization).

Depression Diagnosis and Forecast based on Mobile Phone Sensor Data.

Previous studies have shown the correlation be-tween sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easier to access and less time-consuming. In particular, passive mobile phone data can be collected on a flexible time interval, thus detecting moment-by-moment psychological changes and helping achieve earlier interventions. Moreover, while previous studies mainly focused on depression diagnosis using mobile phone data, depression forecasting has not received sufficient attention. In this work, we extract four types of passive features from mobile phone data, including phone call, phone usage, user activity, and GPS features. We implement a long short-term memory (LSTM) network in a subject-independent 10-fold cross-validation setup to model both a diagnostic and a forecasting tasks. Experimental results show that the forecasting task achieves comparable results with the diagnostic task, which indicates the possibility of forecasting depression from mobile phone sensor data. Our model achieves an accuracy of 77.0 % for major depression forecasting (binary), an accuracy of 53.7 % for depression severity forecasting (5 classes), and a best RMSE score of 4.094 (PHQ-9, range from 0 to 27).

Progress, Opportunities, and Challenges of Magneto-Plasmonic Nanoparticles under Remote Magnetic and Light Stimulation for Brain-Tissue and Cellular Regeneration.

Finding curable therapies for neurodegenerative disease (ND) is still a worldwide medical and clinical challenge. Recently, investigations have been made into the development of novel therapeutic techniques, and examples include the remote stimulation of nanocarriers to deliver neuroprotective drugs, genes, growth factors, and antibodies using a magnetic field and/or low-power lights. Among these potential nanocarriers, magneto-plasmonic nanoparticles possess obvious advantages, such as the functional restoration of ND models, due to their unique nanostructure and physiochemical properties. In this review, we provide an overview of the latest advances in magneto-plasmonic nanoparticles, and the associated therapeutic approaches to repair and restore brain tissues. We have reviewed their potential as smart nanocarriers, including their unique responsivity under remote magnetic and light stimulation for the controlled and sustained drug delivery for reversing neurodegenerations, as well as the utilization of brain organoids in studying the interaction between NPs and neuronal tissue. This review aims to provide a comprehensive summary of the current progress, opportunities, and challenges of using these smart nanocarriers for programmable therapeutics to treat ND, and predict the mechanism and future directions.

Integrated Photocatalytic Reduction and Oxidation of Perfluorooctanoic Acid by Metal-Organic Frameworks: Key Insights into the Degradation Mechanisms.

The high porosity and tunability of metal-organic frameworks (MOFs) have made them an appealing group of materials for environmental applications. However, their potential in the photocatalytic degradation of per- and polyfluoroalkyl substances (PFAS) has been rarely investigated. Hereby, we demonstrate that over 98.9% of perfluorooctanoic acid (PFOA) was degraded by MIL-125-NH2, a titanium-based MOF, in 24 h under Hg-lamp irradiation. The MOF maintained its structural integrity and porosity after three cycles, as indicated by its crystal structure, surface area, and pore size distribution. Based on the experimental results and density functional theory (DFT) calculations, a detailed reaction mechanism of the chain-shortening and H/F exchange pathways in hydrated electron (eaq-)-induced PFOA degradation were revealed. Significantly, we proposed that the coordinated contribution of eaq- and hydroxyl radical (•OH) is vital for chain-shortening, highlighting the importance of an integrated system capable of both reduction and oxidation for efficient PFAS degradation in water. Our results shed light on the development of effective and sustainable technologies for PFAS breakdown in the environment.

Capturing Time Dynamics From Speech Using Neural Networks for Surgical Mask Detection.

The importance of detecting whether a person wears a face mask while speaking has tremendously increased since the outbreak of SARS-CoV-2 (COVID-19), as wearing a mask can help to reduce the spread of the virus and mitigate the public health crisis. Besides affecting human speech characteristics related to frequency, face masks cause temporal interferences in speech, altering the pace, rhythm, and pronunciation speed. In this regard, this paper presents two effective neural network models to detect surgical masks from audio. The proposed architectures are both based on Convolutional Neural Networks (CNNs), chosen as an optimal approach for the spatial processing of the audio signals. One architecture applies a Long Short-Term Memory (LSTM) network to model the time-dependencies. Through an additional attention mechanism, the LSTM-based architecture enables the extraction of more salient temporal information. The other architecture (named ConvTx) retrieves the relative position of a sequence through the positional encoder of a transformer module. In order to assess to which extent both architectures can complement each other when modelling temporal dynamics, we also explore the combination of LSTM and Transformers in three hybrid models. Finally, we also investigate whether data augmentation techniques, such as, using transitions between audio frames and considering gender-dependent frameworks might impact the performance of the proposed architectures. Our experimental results show that one of the hybrid models achieves the best performance, surpassing existing state-of-the-art results for the task at hand.

Homogeneously Mixing Different Metal-Organic Framework Structures in Single Nanocrystals through Forming Solid Solutions.

Pore engineering plays a significant role in the applications of porous materials, especially in the area of separation and catalysis. Here, we demonstrated a metal-organic framework (MOF) solid solution (MOSS) strategy to homogeneously and controllably mix NU-1000 and NU-901 structures inside single MOF nanocrystals. The key for the homogeneous mixing and forming of MOSS was the bidentate modulator, which was designed to have a slightly longer distance between two carboxylate groups than the original tetratopic ligand. All of the MOSS nanocrystals showed a uniform pore size distribution with a well-tuned ratio of mesopores to micropores. Because of the appropriate pore ratio, MOSS nanocrystals can balance the thermodynamic interactions and kinetic diffusion of the substrates, thus showing exceedingly higher separation abilities and a unique elution sequence. Our work proposes a rational strategy to design mixed-porous MOFs with controlled pore ratios and provides a new direction to design homogeneously mixed MOFs with a high separation ability and unique separation selectivity.

Metal-Organic Framework-Based Nanoheater with Photo-Triggered Cascade Effects for On-Demand Suppression of Cellular Thermoresistance and Synergistic Cancer Therapy.

Nanomedicine with stable light-heat conversion and spatiotemporally controllable drug activation is crucial for the success of photothermal therapy (PTT). Herein, a metal-organic framework (MOF)-based nanoheater with light-triggered multi-responsiveness is engineered to in-situ and on-demand sensitize cancer cells to local hyperthermia. Well-dispersed platinum nanoparticles synthesized inside nanospaces of the MOF are employed as the near-infrared (NIR)-harvesting unit with stable and high light-heat conversion performance. A conformation switchable polymer shell is constructed as a secondary light-responding unit to gate the targeted activation of a molecular inhibitor against thermoresistance. By cascade transformation of light stimuli to downstream signals, the nanoheater enables inhibitor release to go with local heating at the same time restricted in lesion sites to maximize efficacy and minimize systemic toxicity. The efficient photothermal conversion and the blockage of cellular heat-protective pathways provide a dual-mode of action which selectively sensitizes cancer cells to hyperthermia in a spatiotemporally controlled manner. With NIR as the remote switch, the MOF-based nanosystem demonstrates localized and boosted PTT efficacy against cancer both in vitro and in vivo. These results present nanosized MOFs as tailorable and versatile platforms for synergistic and precise cancer therapy.

Magnetic nanocomposite hydrogel with tunable stiffness for probing cellular responses to matrix stiffening.

As cells have the capacity to respond to their mechanical environment, cellular biological behaviors can be regulated by the stiffness of extracellular matrix. Moreover, biological processes are dynamic and accompanied by matrix stiffening. Herein, we developed a stiffening cell culture platform based on polyacrylamide-Fe3O4 magnetic nanocomposite hydrogel with tunable stiffness under the application of magnetic field. This platform provided a wide range of tunable stiffness (∼0.3-20 kPa) covering most of human tissue elasticity with a high biocompatibility. Overall, the increased magnetic interactions between magnetic nanoparticles reduced the pore size of the hydrogel and enhanced the hydrogel stiffness, thereby facilitating the adhesion and spreading of stem cells, which was attributed to the F-actin assembly and vinculin recruitment. Such stiffening cell culture platform provides dynamic mechanical environments for probing the cellular response to matrix stiffening, and benefits studies of dynamic biological processes. STATEMENT OF SIGNIFICANCE: Cellular biological behaviors can be regulated by the stiffness of extracellular matrix. Moreover, biological processes are dynamic and accompanied by matrix stiffening. Herein, we developed a stiffening cell culture platform based on polyacrylamide/Fe3O4 magnetic nanocomposite hydrogels with a wide tunable range of stiffness under the application of magnetic field, without adversely affecting cellular behaviors. Such matrix stiffening caused by enhanced magnetic interaction between magnetic nanoparticles under the application of the magnetic field could induce the morphological variations of stem cells cultured on the hydrogels. Overall, our stiffening cell culture platform can be used not only to probe the cellular response to matrix stiffening but also to benefit various biomedical studies.

Nanomechanical sensor for rapid and ultrasensitive detection of tumor markers in serum using nanobody.

Early cancer diagnosis requires ultrasensitive detection of tumor markers in blood. To this end, we develop a novel microcantilever immunosensor using nanobodies (Nbs) as receptors. As the smallest antibody (Ab) entity comprising an intact antigen-binding site, Nbs achieve dense receptor layers and short distances between antigen-binding regions and sensor surfaces, which significantly elevate the generation and transmission of surface stress. Owing to the inherent thiol group at the C-terminus, Nbs are covalently immobilized on microcantilever surfaces in directed orientation via one-step reaction, which further enhances the stress generation. For microcantilever-based nanomechanical sensor, these advantages dramatically increase the sensor sensitivity. Thus, Nb-functionalized microcantilevers can detect picomolar concentrations of tumor markers with three orders of magnitude higher sensitivity, when compared with conventional Ab-functionalized microcantilevers. This proof-of-concept study demonstrates an ultrasensitive, label-free, rapid, and low-cost method for tumor marker detection. Moreover, interestingly, we find Nb inactivation on sensor interfaces when using macromolecule blocking reagents. The adsorption-induced inactivation is presumably caused by the change of interfacial properties, due to binding site occlusion upon complex coimmobilization formations. Our findings are generalized to any coimmobilization methodology for Nbs and, thus, for the construction of high-performance immuno-surfaces. Electronic Supplementary Material: Supplementary material (experimental section, HER2 detection using anti-HER2-mAb-functionalized microcantilevers) is available in the online version of this article at 10.1007/s12274-021-3588-4.