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Chiral discrimination is critical in environmental and life sciences. However, an ideal chiral discrimination strategy has not yet been developed because of the inevitable nonspecific binding entity of wrong enantiomers or insufficient intrinsic optical activities of chiral molecules. Here, we propose an “inspector” recognition mechanism (IRM), which is implemented on a chiral imprinted polydopamine (PDA) layer coated on surface-enhanced Raman scattering (SERS) tag layer. The IRM works based on the permeability change of the imprinted PDA after the chiral recognition and scrutiny of the permeability by an inspector molecule. Good enantiomer can specifically recognize and fully fill the chiral imprinted cavities, whereas the wrong cannot. Then a linear shape aminothiol molecule, as an inspector of the recognition status is introduced, which can only percolate through the vacant and nonspecifically occupied cavities, inducing the SERS signal to decrease. Accordingly, chirality information exclusively stems from good enantiomer specific binding, while nonspecific recognition of wrong enantiomer is curbed. The IRM benefits from sensitivity and versatility, enabling absolute discrimination of a wide variety of chiral molecules regardless of size, functional groups, polarities, optical activities, Raman scattering, and the number of chiral centers. Absolute chiral discrimination in chiral imprinted systems is complicated by the nonspecific binding of enantiomers. Here, the authors report a SERS “inspector” recognition mechanism to distinguish between specifically and nonspecifically bound enantiomers, even in seawater and urine.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents 1 – 5 . Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred 6 . In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures 7 . However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations 8 . Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine–reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health. Research published in Nature shows that surface-enhanced Raman spectroscopy carried out with colloids can quantify a range of molecules down to concentrations at the femtomolar level.

Sensitivity is a crucial parameter for flexible pressure sensors and electronic skins. While introducing microstructures (e.g., micro-pyramids) can effectively improve the sensitivity, it in turn leads to a limited pressure-response range due to the poor structural compressibility. Here, we report a strategy of engineering intrafillable microstructures that can significantly boost the sensitivity while simultaneously broadening the pressure responding range. Such intrafillable microstructures feature undercuts and grooves that accommodate deformed surface microstructures, effectively enhancing the structural compressibility and the pressure-response range. The intrafillable iontronic sensor exhibits an unprecedentedly high sensitivity ( S min   >  220 kPa −1 ) over a broad pressure regime (0.08 Pa-360 kPa), and an ultrahigh pressure resolution (18 Pa or 0.0056%) over the full pressure range, together with remarkable mechanical stability. The intrafillable structure is a general design expected to be applied to other types of sensors to achieve a broader pressure-response range and a higher sensitivity. Though flexible pressure sensors are attractive for next-generation applications, limitations in its performance hinder widespread adoption. Here, the authors report an iontronic flexible pressure sensor with graded intrafillable architecture that shows high sensitivity over a broad pressure range.

Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial for preventing and diagnosing hypertension related diseases. However, current continuous BP monitoring instruments suffer from either bulky systems or poor user-device interfacial performance, hampering their applications in continuous BP monitoring. Here, we report a thin, soft, miniaturized system (TSMS) that combines a conformal piezoelectric sensor array, an active pressure adaptation unit, a signal processing module, and an advanced machine learning method, to allow real wearable, continuous wireless monitoring of ambulatory artery BP. By optimizing the materials selection, control/sampling strategy, and system integration, the TSMS exhibits improved interfacial performance while maintaining Grade A level measurement accuracy. Initial trials on 87 volunteers and clinical tracking of two hypertension individuals prove the capability of the TSMS as a reliable BP measurement product, and its feasibility and practical usability in precise BP control and personalized diagnosis schemes development. Continuous monitoring of arterial blood pressure is limited by bulky connecting systems and poor interfacial contact. Here, Li et al. report a wearable thin, soft, miniaturized system that integrates sensing, active pressure adaptation, and signal processing for improved performance and accuracy.

Commercial wearable piezoelectric sensors possess excellent anti-interference stability due to their electronic packaging. However, this packaging renders them barely breathable and compromises human comfort. To address this issue, we develop a PVDF piezoelectric nanoyarns with an ultrahigh strength of 313.3 MPa, weaving them with different yarns to form three-dimensional piezoelectric fabric (3DPF) sensor using the advanced 3D textile technology. The tensile strength (46.0 MPa) of 3DPF exhibits the highest among the reported flexible piezoelectric sensors. The 3DPF features anti-gravity unidirectional liquid transport that allows sweat to move from the inner layer near to the skin to the outer layer in 4 s, resulting in a comfortable and dry environment for the user. It should be noted that sweating does not weaken the piezoelectric properties of 3DPF, but rather enhances. Additionally, the durability and comfortability of 3DPF are similar to those of the commercial cotton T-shirts. This work provides a strategy for developing comfortable flexible wearable electronic devices. Electronic packaging causes piezoelectric sensors to be airtight, resulting in poor wearing comfort. To address this issue, the authors develop a 3D all-fiber piezoelectric sensor with sweat permeable using the advanced 3D textile technology.

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds 1 , 2 , but has been limited by a lack of suitable protein structures and the complexity of native protein sequence–structure relationships. Here we describe a deep-learning-based ‘family-wide hallucination’ approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine 3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine ( k cat / K m  = 10 6  M −1  s −1 ) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes. A deep-learning-based strategy is used to design artificial luciferases that catalyse the oxidative chemiluminescence of diphenylterazine with high substrate specificity and catalytic efficiency.

Neurotransmitters play essential roles in regulating neural circuit dynamics both in the central nervous system as well as at the peripheral, including the gastrointestinal tract 1 – 3 . Their real-time monitoring will offer critical information for understanding neural function and diagnosing disease 1 – 3 . However, bioelectronic tools to monitor the dynamics of neurotransmitters in vivo, especially in the enteric nervous systems, are underdeveloped. This is mainly owing to the limited availability of biosensing tools that are capable of examining soft, complex and actively moving organs. Here we introduce a tissue-mimicking, stretchable, neurochemical biological interface termed NeuroString, which is prepared by laser patterning of a metal-complexed polyimide into an interconnected graphene/nanoparticle network embedded in an elastomer. NeuroString sensors allow chronic in vivo real-time, multichannel and multiplexed monoamine sensing in the brain of behaving mouse, as well as measuring serotonin dynamics in the gut without undesired stimulations and perturbing peristaltic movements. The described elastic and conformable biosensing interface has broad potential for studying the impact of neurotransmitters on gut microbes, brain–gut communication and may ultimately be extended to biomolecular sensing in other soft organs across the body. NeuroString, a tissue-like biological interface created by laser patterning of polyimide into a graphene/nanoparticle network embedded in an elastomer, is introduced, allowing in vivo real-time detection of neurotransmitters in the brain and gut.

Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases. Early detection of multiple cancers through a single method could be clinically important. Here the authors report the diagnostic performance for early detection for multiple cancers using surface-enhanced Raman spectroscopy (SERS) profiles of exosomes from a single blood test and artificial intelligence in a retrospective study design.

Calcium imaging with protein-based indicators 1 , 2 is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators 3 – 8 . The resulting ‘jGCaMP8’ sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation. Using large-scale screening and structure-guided mutagenesis, fast and sensitive GCaMP sensors are developed and optimized with improved kinetics without compromising sensitivity or brightness.

Molecular profiling of circulating extracellular vesicles (EVs) provides a promising noninvasive means to diagnose, monitor, and predict the course of metastatic breast cancer (MBC). However, the analysis of EV protein markers has been confounded by the presence of soluble protein counterparts in peripheral blood. Here we use a rapid, sensitive, and low-cost thermophoretic aptasensor (TAS) to profile cancer-associated protein profiles of plasma EVs without the interference of soluble proteins. We show that the EV signature (a weighted sum of eight EV protein markers) has a high accuracy (91.1 %) for discrimination of MBC, non-metastatic breast cancer (NMBC), and healthy donors (HD). For MBC patients undergoing therapies, the EV signature can accurately monitor the treatment response across the training, validation, and prospective cohorts, and serve as an independent prognostic factor for progression free survival in MBC patients. Together, this work highlights the potential clinical utility of EVs in management of MBC. A thermophoretic aptasensor can be used to profile cancer-associated proteins of extracellular vesicles (EVs) in patients’ plasma. Here, the authors use this technique to develop an EV-signature able to discriminate metastatic breast cancer, monitor treatment response, and predict patients’ progression-free survival.