The swift progress in wearable technology has accentuated the need for flexible power systems. Such systems are anticipated to exhibit high efficiency, robust durability, consistent power output, and the potential for effortless integration. Integrating ultraflexible energy harvesters and energy storage devices to form an autonomous, efficient, and mechanically compliant power system remains a significant challenge. In this work, we report a 90 µm-thick energy harvesting and storage system (FEHSS) consisting of high-performance organic photovoltaics and zinc-ion batteries within an ultraflexible configuration. With a power conversion efficiency surpassing 16%, power output exceeding 10 mW cm–2, and an energy density beyond 5.82 mWh cm–2, the FEHSS can be tailored to meet the power demands of wearable sensors and gadgets. Without cumbersome and rigid components, FEHSS shows immense potential as a versatile power source to advance wearable electronics and contribute toward a sustainable future. The integration of ultraflexible energy harvesters and energy storage devices to form flexible power systems remains a significant challenge. Here, the authors report a system consisting of organic solar cells and zinc-ion batteries, exhibiting high power output for wearable sensors and gadgets.

Artificial synaptic devices are emerging as contenders for next-generation computing systems due to their combined advantages of self-adaptive learning mechanisms, high parallel computation capabilities, adjustable memory level, and energy efficiency. Optoelectronic devices are particularly notable for their responsiveness to both voltage inputs and light exposure, making them attractive for dynamic modulation. However, engineering devices with reconfigurable synaptic plasticity and multilevel memory within a singular configuration present a fundamental challenge. Here, we have established an organic transistor-based synaptic device that exhibits both volatile and nonvolatile memory characteristics, modulated through gate voltage together with light stimuli. Our device demonstrates a range of synaptic behaviors, including both short/long-term plasticity (STP and LTP) as well as STP-LTP transitions. Further, as an encoding unit, it delivers exceptional read current levels, achieving a program/erase current ratio exceeding 10(5), with excellent repeatability. Additionally, a prototype 4 x 4 matrix demonstrates potential in practical neuromorphic systems, showing capabilities in the perception, processing, and memory retention of image inputs.

Overcoming the mechanical disparities between implantable neural electrodes and biological tissue is crucial in mitigating immune responses, reducing shear motion, and ensuring durable functionality. Emerging hydrogel-based neural interfaces, with their volumetric capacitance, customizable conductivity, and tissue-mimicking mechanical properties, offer a more efficient, less detrimental, and chronically stable alternative to their rigid counterparts. Here, we provide an overview of the exceptional advantages of hydrogels for the development of next-generation neural interfaces and highlight recent advancements that are transforming the field. Materials are needed that can form stable interfaces with neurons, and soft materials are the most promising for this. Here, the advantages and challenges associated with neural interfaces using hydrogels, particularly conductive hydrogels, are discussed.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C=C) location isomer resolution analysis, aided by C=C activation in unsaturated lipids by the Patern & ograve;-B & uuml;chi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C=C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C=C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C=C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

Ammonia (NH3) is one of the most widely used materials in the chemical industry. Here, we report a proof-of-concept study on in situ synthesis and measurement of NH3 from nitrogen by nanoelectrospray-based corona discharge coupled with mass spectrometry. The reactions and analytical confirmations were achieved via polarity-reversing nanoelectrospray ionization (PR-nESI). Plasma was generated by corona discharge at the capillary tip during the negative-polarity half cycle. N-2 was fixed through interfacial reactions and transformed to NH3 in liquid water. NH3 was accumulated within the aqueous solution in the capillary tip. The solution was sprayed out for analytical confirmation during the positive-polarity half cycle. Isotopic labeling confirmed direct conversion of N-15(2) to (NH3)-N-15. The formation of the plasma-water interface was critical to the synthesis of NH3. The addition of a surfactant to the solution could impede the reactions by forming a film at the surface and preventing direct contact between plasma and water.

With the evolution of Industry 4.0, next-generation wearable devices have come under the spotlight, where organic electronics are playing an important role due to their flexible form factor, high performance, and integration capability. Designed to conform to soft and dynamic surfaces, flexible organic photodetectors (OPDs) and integrated arrays stand out for their application potential in non-invasive biosensing and bio-imaging, with inherent advantages in mitigating motion artifacts – a notable limitation of traditional, rigid photodetector systems. Leveraging the near-infrared (NIR) spectrum's capability for deep skin penetration and minimal scattering in human tissues, flexible NIR OPDs are especially capable of precise diagnostics with enhanced signal quality. The adaptable form factor further broadens their potential for applications in human-environment interaction, marking a notable shift towards more responsive and intelligent wearable technologies. This review summarizes the latest challenges and breakthroughs of flexible NIR OPDs, emphasizing effective strategies toward flexible and high-performance device units and integrated systems. We further discuss the remaining challenges and outlook on the vast potential of flexible NIR OPDs in reshaping our interaction with our surroundings.

Corneal stromal is an important structure to maintain corneal transparency. The corneal stroma can be injured by trauma, infection and surgery. Therefore, corneal stromal wound starts repair with phenotype changes in stromal cell, extracellular matrix remodeling and immune cells migration. The corneal scar was the leading cause of blindness worldwide, which can be caused by corneal stromal fibrosis from increased myofibroblasts and deposited extracellular matrix after sever damage. At present, corneal transplantation is the main treatment for corneal scar, which has limited therapeutic effect because of corneal donor shortage, surgical requirements and the risk of postoperative immune rejection. Recently, great progress has been made in the study of control mechanism of corneal stromal wound healing with various molecules, cells and tissues. This paper reviews the repair mechanism of corneal stromal injury and the regulation mechanism of cause of corneal injury, corneal structure and molecule factors towards corneal stromal injury. It aims at providing new ideas for exploring the mechanism of corneal stromal repair and regeneration, which is supposed to help prevent corneal scar clinically.

OBJECTIVE:Mass spectrometry has become the method of choice for single cell analysis due to its high sensitivity of detection and capability in analyzing a large number of metabolites simultaneously. For a long time, an automated and miniaturized system capable of extracting cellular contents from single cells at the pico-liter level for pico-ESI analysis has been lacking.METHODS:This paper presents a first-of-its-kind automated and miniaturized pico-liter extraction system for single-cell MS. The key modules, including imaging, bus controller, and fluidic driving are customized to achieve satisfactory performance at affordable costs, resulting in a miniaturized system movable on a trolley and connectable with the MS. To enable automation, a single cell trapping device, new image-based one-pixel accuracy positioning methods for cells and micropipette, and a surface-tension-based 1-pL accuracy volume control scheme are developed.RESULTS:The system is able to control the solvent loading at 1.97 ± 0.05 nL, solvent dispensing at 14-15 pL, and solvent evaporation at 689±48 pL. MS experiments demonstrate a throughput of 20 cells/h.CONCLUSION:The system has achieved better performance in consistency (∼21%), sensitivity (∼28%), and success rate (up to 40%) than manual operation.SIGNIFICANCE:This automated and miniaturized system lays a solid basis for applying pico-ESI MS analysis in the automated and high-throughput single cell MS analysis, such as single-cell metabolomics and lipidomics.

OBJECTIVE:The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS:A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS:The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION:The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.

智能可穿戴设备结合大数据、云计算等技术,可以实时采集用户健康数据信息和行为习惯,在物联网(IoT)时代的智慧医疗建设进程中将成为获取个人健康信息的重要手段.目前市面上的可穿戴设备,大多为刚性形态,无法顺应皮肤表面纹理,易受运动伪影干扰;另外,大多需要采用腕带等额外固定装置,不可避免地与皮肤产生相对位移,无法保证生理参数测量的重复性要求.

Tattoo electronics, flexible patches that mount directly onto the skin with the ease and flexibility of a temporary tattoo, have foreseen remarkable application potentials in personalized healthcare monitoring and human-machine interfaces. Documented tattoo electronics mostly have an on-skin form factor to date. Using dermal tattoos for in vivo diagnostics presents an unexplored paradigm. Here, we extend the application scenario of "tattoo electrodes" from an on-skin fashion to an actual under-skin configuration, enabled by a highly soft, conductive, and biocompatible polymer matrix. With printed ultrathin electrodes, we achieve a record-high conductivity and record-low skin contact impedance, allowing the design of a single-lead electrocardiogram system for seamless wearing and precise detection of intervals and rhythms in dynamic conditions. Furthermore, the biocompatible ink enables the development of dermal electrodes by virtue of tattoo artistry. It shows enormous potential for electrical communication between bioelectronics and biological tissues at a deep-tissue level.

The study of lipid metabolism relies on the characterization of the lipidome, which is quite complex due to the structure variations of the lipid species. New analytical tools have been developed recently for characterizing fine structures of lipids, with C=C location identification as one of the major improvements. In this study, we studied the lipid metabolism reprograming by analyzing glycerol phospholipid compositions in breast cancer cell lines with structural specification extended to the C=C location level. Inhibition of the lipid desaturase, stearoyl-CoA desaturase 1, increased the proportion of n -10 isomers that are produced via an alternative fatty acid desaturase 2 pathway. However, there were different variations of the ratio of n -9/ n -7 isomers in C18:1-containing glycerol phospholipids after stearoyl-CoA desaturase 1 inhibition, showing increased tendency in MCF-7 cells, MDA-MB-468 cells, and BT-474 cells, but decreased tendency in MDA-MB-231 cells. No consistent change of the ratio of n -9/ n -7 isomers was observed in SK-BR-3 cells. This type of heterogeneity in reprogrammed lipid metabolism can be rationalized by considering both lipid desaturation and fatty acid oxidation, highlighting the critical roles of comprehensive lipid analysis in both fundamental and biomedical applications.

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.

In the fast-evolving landscape of decentralized and personalized healthcare, the need for multimodal biosensing systems that integrate seamlessly with the human body is growing rapidly. This presents a significant challenge in devising ultraflexible configurations that can accommodate multiple sensors and designing high-performance sensing components that remain stable over long periods. To overcome these challenges, ultraflexible organic photodetectors (OPDs) that exhibit exceptional performance under near-infrared illumination while maintaining long-term stability are developed. These ultraflexible OPDs demonstrate a photoresponsivity of 0.53 A W-1 under 940 nm, shot-noise-limited specific detectivity of 3.4 × 1013 Jones, and cut-off response frequency beyond 1 MHz at -3 dB. As a result, the flexible photoplethysmography sensor boasts a high signal-to-noise ratio and stable peak-to-peak amplitude under hypoxic and hypoperfusion conditions, outperforming commercial finger pulse oximeters. This ensures precise extraction of blood oxygen saturation in dynamic working conditions. Ultraflexible OPDs are further integrated with conductive polymer electrodes on an ultrathin hydrogel substrate, allowing for direct interface with soft and dynamic skin. This skin-integrated sensing platform provides accurate measurement of photoelectric and biopotential signals in a time-synchronized manner, reproducing the functionality of conventional technologies without their inherent limitations.

This study highlights the importance of incorporating modern mass spectrometry techniques into undergraduate education. By focusing on the structure-characterizing capability of MS, students are able to gain hands-on experience with cutting-edge techniques and better understand how MS can be used to solve real world problems. The three-step lipid structure analysis strategy presented in the study is a great example of this, as it allows students to understand the process of lipid analysis from headgroup identification to the determination of fatty acyl chain composition and the location of C=C double bonds. The use of chemical derivatization to preactivate a structural moiety for MS/MS analysis is also emphasized in the study. This provides students with a deeper understanding of the limitations of MS/MS analysis and how chemical modifications can be used to overcome these limitations. The demonstration of a [2 + 2] photochemical reaction to locate a C=C in unsaturated lipids is a practical example of this concept. Overall, this study supports the idea that incorporating advanced research techniques into undergraduate education can not only enhance students' understanding of scientific instruments but also promote their interests in research and innovation.

Flexible electronics capable of interacting with biological tissues, and acquiring and processing biological information, are increasingly demanded to capture the dynamic physiological processes, understand the living organisms, and treat human diseases. Neural interfaces with a high spatiotemporal resolution, extreme mechanical compliance, and biocompatibility are essential for precisely recording brain activity and localizing neuronal patterns that generate pathological brain signals. Organic transistors possess unique advantages in detecting low-amplitude signals at the physiologically relevant time scales in biotic environments, given their inherent amplification capabilities for in situ signal processing, designable flexibility, and biocompatibility features. This review summarizes recent progress in neural activity recording and stimulation enabled by flexible and stretchable organic transistors. We introduce underlying mechanisms for multiple transistor building blocks, followed by an explicit discussion on effective design strategies toward flexible and stretchable organic transistor arrays with improved signal transduction capabilities at the transistor/neural interfaces.

On-skin electronics that offer revolutionary capabilities in personalized diagnosis, therapeutics, and human-machine interfaces require seamless integration between the skin and electronics. A common question remains whether an ideal interface can be introduced to directly bridge thin-film electronics with the soft skin, allowing the skin to breathe freely and the skin-integrated electronics to function stably. Here, an ever-thinnest hydrogel is reported that is compliant to the glyphic lines and subtle minutiae on the skin without forming air gaps, produced by a facile cold-lamination method. The hydrogels exhibit high water-vapor permeability, allowing nearly unimpeded transepidermal water loss and free breathing of the skin underneath. Hydrogel-interfaced flexible (opto)electronics without causing skin irritation or accelerated device performance deterioration are demonstrated. The long-term applicability is recorded for over one week. With combined features of extreme mechanical compliance, high permeability, and biocompatibility, the ultrathin hydrogel interface promotes the general applicability of skin-integrated electronics.

Lipids are among the four categories of biomolecules, and their structural diversity is closely related to lipid functions and fundamental biology. This chapter aims to give a brief overview of the recently developed mass spectrometry (MS) methods for comprehensive lipid characterization, in particular the localization of carbon–carbon double bonds (CC) in a variety of lipid classes, using novel ion activation and chemical derivatization approaches. Together, these methodological advancements have brought lipid analysis to an unprecedented level of structural detail, thereby laying the foundation for the study of lipid biology and relevant biomedical applications.

敞开式离子化质谱是一种新兴的质谱快检技术,具有分析速度快、操作流程简单、特异性强等特点.该技术无需对样品做复杂预处理,尤其在与便携式质谱仪联用时,可用于大量样品的现场、快速、准确筛查,在贸易产品化学风险物质的筛查中展现出广泛的应用前景.该文主要综述敞开式离子化质谱近年来在贸易产品化学风险物质筛查方面的研究进展,并对其未来的发展前景进行了展望.