Ionic Rectification by Dynamic Regulation of the Electric Double Layer at the Hydrogel Interface.

Hydrogels play a pivotal role in the realm of iontronics, contributing to the realization of futuristic human-machine interactions. The electric double layer (EDL) between the hydrogel and electrode provides an essential ionic-electronic coupling interface. While prior investigations primarily delved into elucidating the formation mechanism of the EDL, our study shifts the focus to showcasing the current generation through the mechanical modulation of the EDL at the hydrogel-metal interfaces. The dynamic EDL was constructed by the mechano-driven contact-separation process between the polyacrylamide (PAAm) hydrogel and Au. Influencing factors on the dynamic regulation of the EDL such as ion concentration, types of salt, contact-separation frequency, and deformation degree were investigated. Dehydration usually limits the practical applications of hydrogels, and it is a long-standing and difficult problem. However, it seemed to be able to slow the EDL formation process here, resulting in a sustained continuous direct current signal output. Such hydrogel iontronics could rectify the displacement electronic current of a triboelectric nanogenerator by the ionic current. The directional migration of ions could be further enhanced by using charge-collecting metals with different work functions, for example, Au and Al. It offers a paradigm to enable ionic rectification that could be seamlessly incorporated into electronic systems, ushering in a new era for efficient energy harvesting and biomimetic nervous systems.

Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae.

Carbapenem-resistant Enterobacteriaceae (CRE) is a major pathogen that poses a serious threat to human health. Unfortunately, currently, there are no effective measures to curb its rapid development. To address this, an in-depth study on the surface-enhanced Raman spectroscopy (SERS) of 22 strains of 7 categories of CRE using a gold silver composite SERS substrate was conducted. The residual networks with an attention mechanism to classify the SERS spectrum from three perspectives (pathogenic bacteria type, enzyme-producing subtype, and sensitive antibiotic type) were performed. The results show that the SERS spectrum measured by the composite SERS substrate was repeatable and consistent. The SERS spectrum of CRE showed varying degrees of species differences, and the strain difference in the SERS spectrum of CRE was closely related to the type of enzyme-producing subtype. The introduced attention mechanism improved the classification accuracy of the residual network (ResNet) model. The accuracy of CRE classification for different strains and enzyme-producing subtypes reached 94.0% and 96.13%, respectively. The accuracy of CRE classification by pathogen sensitive antibiotic combination reached 93.9%. This study is significant for guiding antibiotic use in CRE infection, as the sensitive antibiotic used in treatment can be predicted directly by measuring CRE spectra. Our study demonstrates the potential of combining SERS with deep learning algorithms to identify CRE without culture labels and classify its sensitive antibiotics. This approach provides a new idea for rapid and accurate clinical detection of CRE and has important significance for alleviating the rapid development of resistance to CRE.

Association between abnormal lipid metabolism and tumor.

Metabolic Reprogramming is a sign of tumor, and as one of the three major substances metabolism, lipid has an obvious impact. Abnormal lipid metabolism is related to the occurrence of various diseases, and the proportion of people with abnormal lipid metabolism is increasing year by year. Lipid metabolism is involved in the occurrence, development, invasion, and metastasis of tumors by regulating various oncogenic signal pathways. The differences in lipid metabolism among different tumors are related to various factors such as tumor origin, regulation of lipid metabolism pathways, and diet. This article reviews the synthesis and regulatory pathways of lipids, as well as the research progress on cholesterol, triglycerides, sphingolipids, lipid related lipid rafts, adipocytes, lipid droplets, and lipid-lowering drugs in relation to tumors and their drug resistance. It also points out the limitations of current research and potential tumor treatment targets and drugs in the lipid metabolism pathway. Research and intervention on lipid metabolism abnormalities may provide new ideas for the treatment and survival prognosis of tumors.

Selenium and thyroid diseases.

Selenium, a non-metallic element, is a micronutrient essential for the biosynthesis of selenoproteins containing selenocysteine. In adults, the thyroid contains the highest amount of selenium per gram of tissue. Most known selenoproteins, such as glutathione peroxidase, are expressed in the thyroid and are involved in thyroid hormone metabolism, redox state regulation, and maintenance of cellular homeostasis. Some clinical studies have shown that lack of selenium will increase the prevalence of several kinds of thyroid diseases. Selenium treatment in patients with Graves' orbitopathy has been shown to delay disease progression and improve the quality of life. Selenium supplementation in Hashimoto's thyroiditis was associated with the decreased levels of anti-thyroid peroxidase antibody and improved thyroid ultrasound structure. In thyroid cancer, various selenium supplements have shown variable anticancer activity. However, published results remain the conflicting and more clinical evidence is still needed to determine the clinical significance of selenium. This article reviews the strong association between selenium and thyroid disease and provides new ideas for the clinical management of selenium in thyroid disease.

Raman spectroscopy combined with deep learning for rapid detection of melanoma at the single cell level.

Melanoma is an aggressive and metastatic skin cancer caused by genetic mutations in melanocytes, and its incidence is increasing year by year. Understanding the gene mutation information of melanoma cases is very important for its precise treatment. The current diagnostic methods for melanoma include radiological, pharmacological, histological, cytological and molecular techniques, but the gold standard for diagnosis is still pathological biopsy, which is time consuming and destructive. Raman spectroscopy is a rapid, sensitive and nondestructive detection method. In this study, a total of 20,000 Surface-enhanced Raman scattering (SERS) spectra of melanocytes and melanoma cells were collected using a positively charged gold nanoparticles planar solid SERS substrate, and a classification network system based on convolutional neural networks (CNN) was constructed to achieve the classification of melanocytes and melanoma cells, wild-type and mutant melanoma cells and their drug resistance. Among them, the classification accuracy of melanocytes and melanoma cells was over 98%. Raman spectral differences between melanocytes and melanoma cells were analyzed and compared, and the response of cells to antitumor drugs were also evaluated. The results showed that Raman spectroscopy provided a basis for the medication of melanoma, and SERS spectra combined with CNN classification model realized classification of melanoma, which is of great significance for rapid diagnosis and identification of melanoma.

Standardized measurement of dielectric materials' intrinsic triboelectric charge density through the suppression of air breakdown.

Triboelectric charge density and energy density are two crucial factors to assess the output capability of dielectric materials in a triboelectric nanogenerator (TENG). However, they are commonly limited by the breakdown effect, structural parameters, and environmental factors, failing to reflect the intrinsic triboelectric behavior of these materials. Moreover, a standardized strategy for quantifying their maximum values is needed. Here, by circumventing these limitations, we propose a standardized strategy employing a contact-separation TENG for assessing a dielectric material's maximum triboelectric charge and energy densities based on both theoretical analyses and experimental results. We find that a material's vacuum triboelectric charge density can be far higher than previously reported values, reaching a record-high of 1250 µC m-2 between polyvinyl chloride and copper. More importantly, the obtained values for a dielectric material through this method represent its intrinsic properties and correlates with its work function. This study provides a fundamental methodology for quantifying the triboelectric capability of dielectric materials and further highlights TENG's promising applications for energy harvesting.

Fast Synthesis of Au Nanoparticles on Metal-Phenolic Network for Sweat SERS Analysis.

The biochemical composition of sweat is closely related to the human physiological state, which provides a favorable window for the monitoring of human health status, especially for the athlete. Herein, an ultra-simple strategy based on the surface-enhanced Raman scattering (SERS) technique for sweat analysis is established. Metal-phenolic network (MPN), an outstanding organic-inorganic hybrid material, is adopted as the reductant and platform for the in situ formation of Au-MPN, which displays excellent SERS activity with the limit of detection to 10-15 M for 4-mercaptobenzoic acid (4-MBA). As an ultrasensitive SERS sensor, Au-MPN is capable of discriminating the molecular fingerprints of sweat components acquired from a volunteer after exercise, such as urea, uric acid, lactic acid, and amino acid. For pH sensing, Au-MPN/4-MBA efficiently presents the pH values of the volunteer's sweat, which can indicate the electrolyte metabolism during exercise. This MPN-based SERS sensing strategy unlocks a new route for the real-time physiological monitoring of human health.

Combining fiber optical tweezers and Raman spectroscopy for rapid identification of melanoma.

Cutaneous melanoma is a skin tumor with a high degree of malignancy and fatality rate, the incidence of which has increased in recent years. Therefore, a rapid and sensitive diagnostic technique of melanoma cells is urgently needed. In this paper, we present a new approach using fiber optical tweezers to manipulate melanoma cells to measure their Raman spectra. Then, combined with Principal Component Analysis and Support Vector Machines (PCA-SVM) classification model, to achieve the classification of common mutant, wild-type and drug-resistant melanoma cells. A total of 150 Raman spectra of 30 cells were collected from mutant, wild-type and drug-resistant melanoma cell lines, and the classification accuracy was 92%, 94%, 97.5%, respectively. These results suggest that the study of tumor cells based on fiber optical tweezers and Raman spectroscopy is a promising method for early and rapid identification and diagnosis of tumor cells.

Achieving Ultrahigh Effective Surface Charge Density of Direct-Current Triboelectric Nanogenerator in High Humidity.

As an emerging energy-harvesting technology, the triboelectric nanogenerator (TENG) is considered a powerful driving force toward the new-era of Internet of Things and artificial intelligence, but its output performance is dramatically influenced by environmental humidity. Herein, a direct current TENG (DC-TENG) based on the triboelectrification effect and electrostatic breakdown is reported to address the problem of output attenuation in high humidity environments for the conventional TENGs. It is found that high humidity not only enhances the sliding triboelectrification effect of hydrophobic triboelectric materials, but also promotes the electrostatic breakdown process for DC-TENG, thus contributing to the improvement of DC-TENG output. Furthermore, taking poly(vinyl chloride) film as the friction layer, the effective surface charge density of DC-TENG with microstructure-designed electrode achieves a milestone value of ≈2.97 mC m-2 under 90% relative humidity, which is almost 1.42-fold larger than that under 30% RH. This work not only establishes an effective methodology to boost the output performance of TENG in a high humidity environment, but also establishes a foundation for its practical applications in large-scale energy harvesting.

Facile synthesis of Ag-niobium ditelluride nanocomposites for the molecular fingerprint analysis of muscle tissues.

The surface-enhanced Raman scattering (SERS) technique has attracted increasing attention in biomedicine due to the capability of providing the molecular fingerprint information of biosamples. Two-dimensional (2D) metal nanohybrids have proven to be efficient SERS substrates for bioanalysis due to the cooperative Raman enhancement mechanism. Herein, a facile strategy for the fabrication of a novel transition metal telluride-based 2D-metal SERS substrate is proposed. Ag-niobium ditelluride (NbTe2) nanocomposites are synthesized through in situ reduction of Ag nanoparticles on NbTe2 nanosheets (NSs). The excellent SERS performance of Ag-NbTe2 NSs probably originates from the coupling effect of enhanced electromagnetic field distribution around the plasmonic nanostructure (hot spots) and the charge transfer caused by the semiconductor material after numerical simulations. Using Ag-NbTe2 NSs as an ultrasensitive SERS probe, the molecular components of three types of muscle tissues are clearly identified. An effective classification of skeletal, cardiac and smooth muscles is also realized according to their SERS data plus principal component analysis. In addition, the monitoring of the skeletal muscle stretching process is achieved by the Ag-NbTe2 NS-based SERS analysis, which represents a powerful tool for molecular exercise physiology.

A Tuning-Fork Triboelectric Nanogenerator with Frequency Multiplication for Efficient Mechanical Energy Harvesting.

As a new technology for high-entropy energy harvesting, a triboelectric nanogenerator (TENG) has broad applications in sensor networks and internet of things as a power source, but its average power density is limited by the fixed low-frequency output. Here, a frequency-multiplication TENG based on intrinsic high frequency of tuning fork is proposed which enables converting low-frequency mechanical energy into high-frequency electric energy. A tuning-fork TENG is used to systematically study the effects of intrinsic frequency, dielectric's thickness, and gap distance on its electric performance, and a total transferred charges of 4.3 µC and an average power density of 9.42 mW m-2 are realized at the triggering frequency of 0.2 Hz, which are 71 times and 5.7 times than that of the single-cycle output of conventional contact-separation TENG, respectively. Moreover, the crest factor also decreases from 3.5 to around 1.5. Then, a homemade tuning fork-like TENG is reasonably designed for harvesting ambient wind energy, achieving an average power density of 20.02 mW m-2 at a wind speed of 7 m s-1 . Specially, its impedance resistance is independent of the mechanical triggering frequency, simplifying the back-end power management circuit design. Therefore, the frequency-multiplication TENG shows a great potential for efficient distributed energy harvesting.

A Self-Powered Dual-Type Signal Vector Sensor for Smart Robotics and Automatic Vehicles.

Automatic control systems are the most efficient technology for reducing labor cost while improving work efficiency. Vector motion monitoring is indispensable for the normal operation of automatic control systems. Here, a self-powered dual-type signal triboelectric nanogenerator (DS-TENG) is designed through integrating an alternating-current TENG and a direct-current TENG, which can monitor vector movement in real time based on pulse signal counts. As a result, the DS-TENG avoids the shortcoming of traditional self-powered sensors based on signal amplitude that is sensitive to the working environment, achieves a high sensing precision, and maintains stability after reciprocating motion of 500 000 cycles. Moreover, it realizes effective movement direction recognition by self-powered switching of signal type in reverse movement. This dual-type signal TENG exhibits high precision and automatic direction recognition in vector motion monitor and trajectory tracker, paving the way for the application of the self-powered TENG sensor in automatic control systems in the future.

[Clinical and genetic analysis of three children patients with Kleefstra syndrome].

OBJECTIVE: To explore the genetic basis of three children with unexplained developmental delay/intellectual disability (DD/ID). METHODS: Peripheral blood samples were collected from the patients and subjected to chromosomal microarray analysis (CMA). RESULTS: Patient 1 was found to harbor a 190 kb deletion at 9q34.3, which encompassed most of EHMT1 (OMIM 607001), the key gene for Kleefstra syndrome (OMIM 610253). Patients 2 and 3 were siblings. CMA showed that they have shared four chromosomal copy number variations (CNVs) including a deletion at 9q34.3 which spanned 154 kb and 149 kb, respectively, and encompassed the EHMT1 and CACNA1B (OMIM 601012) genes. The remaining 3 CNVs were predicted to be with no clinical significance. CONCLUSION: Microdeletions at 9q33.4 probably underlay the pathogenesis of DD/ID in the three children, for which EHMT1 may be the key gene.

Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis.

The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.

Selection rules of triboelectric materials for direct-current triboelectric nanogenerator.

The rapid development of Internet of Things and artificial intelligence brings increasing attention on the harvesting of distributed energy by using triboelectric nanogenerator (TENG), especially the direct current TENG (DC-TENG). It is essential to select appropriate triboelectric materials for obtaining a high performance TENG. In this work, we provide a set of rules for selecting the triboelectric materials for DC-TENG based on several basic parameters, including surface charge density, friction coefficient, polarization, utilization rate of charges, and stability. On the basis of the selection rules, polyvinyl chloride, used widely in industry rather than in TENG, is selected as the triboelectric layer. Its effective charge density can reach up to ~8.80 mC m-2 in a microstructure-designed DC-TENG, which is a new record for all kinds of TENGs. This work can offer a basic guideline for the triboelectric materials selection and promote the practical applications of DC-TENG.

Low-Cost, Environmentally Friendly, and High-Performance Triboelectric Nanogenerator Based on a Common Waste Material.

With the great progress in human activities and production technologies, the waste inevitably produced causes not only environmental pollution but also resource waste; meanwhile, the mobile and portable electronic devices urgently need a distributed and sustainable energy source to ensure their stable operation. Here, the waste pollutants (milk cartons) generated from daily life, commonly associated with environmental concerns, are instead identified as an available resource for preparing an emerging energy harvester (triboelectric nanogenerator, TENG), which can convert ubiquitous mechanical energy into electric power. Consequently, based on the waste material, the initial charge density of the TENG is as low as 0.035 mC m-2, which can be tremendously improved to 1.00 mC m-2 through combining a charge excitation circuit, achieving efficient energy harvesting. In addition, compared to the common dielectric film, the waste material can reduce the cost and simplify the process of the preparation of TENG. This work provides not only an innovative approach to simultaneously realize environmental protection and energy harvesting but also more material choice for the preparation of a low-cost and high-performance TENG.

Spin-Torque Memristors Based on Perpendicular Magnetic Tunnel Junctions for Neuromorphic Computing.

Spin-torque memristors are proposed in 2009, and can provide fast, low-power, and infinite memristive behavior for neuromorphic computing and large-density non-volatile memory. However, the strict requirements of combining high magnetoresistance, stable domain wall pinning and current-induced switching in a single device pose difficulties in physical implementation. Here, a nanoscale spin-torque memristor based on a perpendicular-anisotropy magnetic tunnel junction with a CoFeB/W/CoFeB composite free layer structure is experimentally demonstrated. Its tunneling magnetoresistance is higher than 200%, and memristive behavior can be realized by spin-transfer torque switching. Memristive states are retained by strong domain wall pinning effects in the free layer. Experiments and simulations suggest that nanoscale vertical chiral spin textures can form around clusters of W atoms under the combined effect of opposite Dzyaloshinskii-Moriya interactions and the Ruderman-Kittel-Kasuya-Yosida interaction between the two CoFeB free layers. Energy fluctuation caused by these textures may be the main reason for the strong pinning effect. With the experimentally demonstrated memristive behavior and spike-timing-dependent plasticity, a spiking neural network to perform handwritten pattern recognition in an unsupervised manner is simulated. Due to advantages such as long endurance and high speed, the spin-torque memristors are competitive in the future applications for neuromorphic computing.

Rationally patterned electrode of direct-current triboelectric nanogenerators for ultrahigh effective surface charge density.

As a new-era of energy harvesting technology, the enhancement of triboelectric charge density of triboelectric nanogenerator (TENG) is always crucial for its large-scale application on Internet of Things (IoTs) and artificial intelligence (AI). Here, a microstructure-designed direct-current TENG (MDC-TENG) with rationally patterned electrode structure is presented to enhance its effective surface charge density by increasing the efficiency of contact electrification. Thus, the MDC-TENG achieves a record high charge density of ~5.4 mC m-2, which is over 2-fold the state-of-art of AC-TENGs and over 10-fold compared to previous DC-TENGs. The MDC-TENG realizes both the miniaturized device and high output performance. Meanwhile, its effective charge density can be further improved as the device size increases. Our work not only provides a miniaturization strategy of TENG for the application in IoTs and AI as energy supply or self-powered sensor, but also presents a paradigm shift for large-scale energy harvesting by TENGs.

A Fully Self-Powered Vibration Monitoring System Driven by Dual-Mode Triboelectric Nanogenerators.

Vibration sensor is very necessary for monitoring the structural health of constructions. However, it is still a major challenge to meet simultaneously real-time monitoring, continuous assessment, and early incident warning in a simple device without a complicated power and analysis system. Here, we report a self-powered vibration sensor system to achieve real-time and continuous detection of the vibration characteristics from a dual-mode triboelectric nanogenerator (AC/DC-TENG), which can produce either alternating current (AC) or direct current (DC) within different operation zones. Within the vibration-safe region, the AC/DC-TENG with AC output not only can continuously assess the vibration characteristics but also can power the signal transmission. More importantly, once the vibration amplitude crosses the danger threshold, the AC converts immediately to DC, meanwhile triggering the alarm system directly to accurately predict the danger of construction. Our self-powered vibration sensor system can serve as a facile tool for accurately monitoring the structural health of constructions.

Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization.

Many biological experimental studies have confirmed that microRNAs (miRNAs) play a significant role in human complex diseases. Exploring miRNA-disease associations could be conducive to understanding disease pathogenesis at the molecular level and developing disease diagnostic biomarkers. However, since conducting traditional experiments is a costly and time-consuming way, plenty of computational models have been proposed to predict miRNA-disease associations. In this study, we presented a neoteric Bayesian model (KBMFMDA) that combines kernel-based nonlinear dimensionality reduction, matrix factorization and binary classification. The main idea of KBMFMDA is to project miRNAs and diseases into a unified subspace and estimate the association network in that subspace. KBMFMDA obtained the AUCs of 0.9132, 0.8708, 0.9008±0.0044 in global and local leave-one-out and five-fold cross validation. Moreover, KBMFMDA was applied to three important human cancers in three different kinds of case studies and most of the top 50 potential disease-related miRNAs were confirmed by many experimental reports.