Microfluidic sweat patch based on capillary force and evaporation pump for real-time continuous sweat analysis.

In addition to the common blood and urine, fresh sweat contains a diverse range of physiological indicators that can effectively reflect changes in the body's state. Wearable sweat sensors are crucial for understanding human physiological health; however, real-time in situ measurement of multiple biomarkers in sweat remains a significant challenge. Here, we propose a wearable microfluidic patch featuring an integrated microfluidic channel and evaporation pump for accelerated and continuous sweat collection, eliminating the need for additional sweat storage cavities that typically impede real-time detection. Capillary forces are harnessed to facilitate the rapid flow of sweat through the detection area, while an evaporation pump based on porous laser-induced graphene enhances sweat evaporation. The synergistic integration of these two components enables an uninterrupted flow of fresh sweat within the patch, ensuring real-time monitoring. The influence of channel size parameters on sweat flow velocity is analyzed, and the optimal width-to-height ratio for achieving the desired flow velocity is determined. By implementing a multi-channel parallel design with chamfering, liquid flow resistance is effectively reduced. Furthermore, the patch integrates sensor modules for sodium ion, chloride ion, glucose, and pH value measurements, ensuring excellent sealing and stability of the assembled system. This work presents a simplified approach to developing wearable sweat sensors that hold the potential for health monitoring and disease diagnosis.

ROS-responsive hydrogels: from design and additive manufacturing to biomedical applications.

Hydrogels with intricate 3D networks and high hydrophilicity have qualities resembling those of biological tissues, making them ideal candidates for use as smart biomedical materials. Reactive oxygen species (ROS) responsive hydrogels are an innovative class of smart hydrogels, and are cross-linked by ROS-responsive modules through covalent interactions, coordination interactions, or supramolecular interactions. Due to the introduction of ROS response modules, this class of hydrogels exhibits a sensitive response to the oxidative stress microenvironment existing in organisms. Simultaneously, due to the modularity of the ROS-responsive structure, ROS-responsive hydrogels can be manufactured on a large scale through additive manufacturing. This review will delve into the design, fabrication, and applications of ROS-responsive hydrogels. The main goal is to clarify the chemical principles that govern the response mechanism of these hydrogels, further providing new perspectives and methods for designing responsive hydrogel materials.

Polyphenolic Platform Ameliorated Sanshool for Skin Photoprotection.

Natural evolution has nurtured a series of active molecules that play vital roles in physiological systems, but their further applications have been severely limited by rapid deactivation, short cycle time, and potential toxicity after isolation. For instance, the instability of structures and properties has greatly descended when sanshool is derived from Zanthoxylum xanthoxylum. Herein, natural polyphenols are employed to boost the key properties of sanshool by fabricating a series of nanoparticles (NPs). The intracellular evaluation and in vivo animal model are conducted to demonstrate the decreased photodamage score and skin-fold thickness of prepared NPs, which can be attributed to the better biocompatibility, improved free radical scavenging, down-regulated apoptosis ratios, and reduced DNA double-strand breaks compared to naked sanshool. This work proposes a novel strategy to boost the key properties of naturally occurring active molecules with the assistance of natural polyphenol-based platforms.

Natural polyphenolic antibacterial bio-adhesives for infected wound healing.

Bio-adhesives used clinically, commonly have the ability to fill surgical voids and support wound healing, but which are devoid of antibacterial activity, and thus, could not meet the particular needs of the infected wound site. Herein, a series of natural polyphenolic antibacterial bio-adhesives were prepared via simple mixing and heating of polyphenols and acid anhydrides without any solvent or catalyst. Upon the acid anhydride ring opening and acylation reactions, various natural polyphenolic bio-adhesives could adhere to various substrates (i.e., tissue, wood, glass, rubber, paper, plastic, and metal) based on multi-interactions. Moreover, these bio-adhesives showed excellent antibacterial and anti-infection activity, rapid hemostatic performance and appropriate biodegradability, which could be widely used in promoting bacterial infection wound healing and hot burn infection wound repair. This work could provide a new strategy for strong adhesives using naturally occurring molecules, and provide a method for the preparation of novel multifunctional wound dressings for infected wound healing.

Biomimetic Polyphenolic Scaffolds with Antioxidative Abilities for Improved Bone Regeneration.

Bone defects have a severe impact on the health and lives of patients due to their long-lasting and difficult-to-treat features. Recent studies have shown that there are complex microenvironments, including excessive production of reactive oxygen species. Herein, a surface functionalization strategy using metal-polyphenolic networks was used, which was found to be beneficial in restoring oxidative balance and enhancing osseointegration. The surface properties, biocompatibility, intracellular ROS scavenging, and osseointegration capacity were evaluated, and the therapeutic effects were confirmed using a skull defect model. This approach has great potential to improve complex microenvironments and enhance the efficiency of bone tissue regeneration.

A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network.

Two-terminal self-rectifying (SR)-synaptic memristors are preeminent candidates for high-density and efficient neuromorphic computing, especially for future three-dimensional integrated systems, which can self-suppress the sneak path current in crossbar arrays. However, SR-synaptic memristors face the critical challenges of nonlinear weight potentiation and steep depression, hindering their application in conventional artificial neural networks (ANNs). Here, a SR-synaptic memristor (Pt/NiOx/WO3-x:Ti/W) and cross-point array with sneak path current suppression features and ultrahigh-weight potentiation linearity up to 0.9997 are introduced. The image contrast enhancement and background filtering are demonstrated on the basis of the device array. Moreover, an unsupervised self-organizing map (SOM) neural network is first developed for orientation recognition with high recognition accuracy (0.98) and training efficiency and high resilience toward both noises and steep synaptic depression. These results solve the challenges of SR memristors in the conventional ANN, extending the possibilities of large-scale oxide SR-synaptic arrays for high-density, efficient, and accurate neuromorphic computing.

All-Optically Controlled Artificial Synapses Based on Light-Induced Adsorption and Desorption for Neuromorphic Vision.

Artificial synapses with the capability of optical sensing and synaptic functions are fundamental components to construct neuromorphic visual systems. However, most reported artificial optical synapses require a combination of optical and electrical stimuli to achieve bidirectional synaptic conductance modulation, leading to an increase in the processing time and system complexity. Here, an all-optically controlled artificial synapse based on the graphene/titanium dioxide (TiO2) quantum dot heterostructure is reported, whose conductance could be reversibly tuned by the effects of light-induced oxygen adsorption and desorption. Synaptic behaviors, such as excitatory and inhibitory, short-term and long-term plasticity, and learning-forgetting processes, are implemented using the device. An artificial neural network simulator based on the artificial synapse was used to train and recognize handwritten digits with a recognition rate of 92.2%. Furthermore, a 5 × 5 optical synaptic array that could simultaneously sense and memorize light stimuli was fabricated, mimicking the sensing and memory functionality of the retina. Such an all-optically controlled artificial synapse shows a promising prospect in the application of perception, learning, and memory tasks for future neuromorphic visual systems.

A Minimum Cost Consensus-Based Failure Mode and Effect Analysis Framework Considering Experts' Limited Compromise and Tolerance Behaviors.

This study proposes a minimum cost consensus-based failure mode and effect analysis (MCC-FMEA) framework considering experts' limited compromise and tolerance behaviors, where the first behavior indicates that a failure mode and effect analysis (FMEA) expert might not tolerate modifying his/her risk assessment without limitations, and the second behavior indicates that an FMEA expert will accept risk assessment suggestions without being paid for any cost if the suggested risk assessments fall within his/her tolerance threshold. First, an MCC-FMEA with limited compromise behaviors is presented. Second, experts' tolerance behaviors are added to the MCC-FMEA with limited compromise behaviors. Theoretical results indicate that in some cases, this MCC-FMEA with limited compromise and tolerance behaviors has no solution. Thus, a minimum compromise adjustment consensus model and a maximum consensus model with limited compromise behaviors are developed and analyzed, and an interactive MCC-FMEA framework, resulting in an FMEA problem consensual collective solution, is designed. A case study, regarding the assessment of COVID-19-related risk in radiation oncology, and a detailed sensitivity and comparative analysis with the existing FMEA approaches are provided to verify the effectiveness of the proposed approach to FMEA consensus-reaching.

Quantitative determination of cyanide residues in polysaccharide-protein conjugate vaccines based on flow injection analysis(FIA) with amperometric detector / 中国生物制品学杂志

@#Objective To develop and verify a method for determination of cyanide residues in polysaccharide-protein conjugate vaccines based on flow injection analysis(FIA)with amperometric detector.Methods After removing macromolecular substances in the samples by ultrafiltration,the cyanide residues were determined by a 3700 automatic chemical analyzer with the injection time of 35 s,injection volume of 200 μL,pump speed of 40%,sample cycle time of 140 s,ultraviolet wavelength of 312 nm and an Ampere detector.The developed method was verified for the specificity,matrix effect,linear range,limit of detection(LOD),limit of quantitation(LOQ),accuracy,presicion and stability.The cyanide residues in the polysaccharide derivative bulk(13 batches)and conjugate bulk(21 batches)of Haemophilus influenzae type b conjugate vaccine and group A and C meningococcal conjugate vaccine produced by 5 manufacturers were determined by the developed method.Results Blank samples showed no interference to detection;The recoveries of the matrix effector solution of Haemophilus influenzae type b conjugate vaccine polysaccharide derivative and conjugate bulk,group A and C meningococcal conjugate vaccine polysaccharide derivative and conjugate bulk were 97.4%,102.4%,96.8% and 99.8% respectively,with all CV values less than 15%;In the range of 0.312 5 ～ 80 ng/mL,cyanogroup concentration showed a good linear relationship with peak height with a regression equation of y = 133.13 x + 57.556,R2= 0.999 1;The LOD was 0.2 ng/mL,and the LOQ was 0.6 ng/mL.The average recoveries of the control solution were 108.9%,106.5%,103.5% respectively with RSD value of 6.4% in the groups added with 5,10 and 20 ng/mL of cyanogroup and the CV values of precision verification were all less than 15%;The average concentrations of the control solution injected 20 times continuously were 76,38,18and 5 ng/mL with all CV values less than 15%,when cyanogroup concentration was 80,40,20 and 5 ng/mL,respectively.Cyanide residues were detected in 13 batches of derivative bulk samples,while not in 2 batches of 21 batches of polysaccharide conjugate bulk samples,and were detected in the others.Conclusion The developed method had good accuracy,precision and stability,which might be applied to the quantitative determination of cyanide residues in polysaccharideprotein conjugate vaccines.

Antioxidative myricetin-enriched nanoparticles towards acute liver injury.

Acute liver injury (ALI) could severely destroy the liver function and cause inevitable damage to human health. Studies have demonstrated that excessive reactive oxygen species (ROS) and accompanying inflammatory factors play vital roles in the ALI disease. Herein, we fabricated a kind of nature-inspired myricetin-enriched nanomaterial via Michael addition and Schiff base reaction, which possessed uniform morphology, tunable component ratios, great stabilities, promising free radical scavenging abilities, biocompatibility and protective effects towards cells under oxidative stress. Additionally, the therapeutic effects were demonstrated using an ALI model by down-regulating ROS and inflammatory levels and restoring the liver function. This study could provide a strategy to construct robust and antioxidative nanomaterials using naturally occurring molecules against intractable diseases.

Wearable microfluidic patch with integrated capillary valves and pumps for sweat management and multiple biomarker analysis.

Wearable sweat sensors are essential for providing insight into human physiological health. The currently developed microfluidic sweat sensors have demonstrated the function of collecting and storing sweat. However, they detect more average concentrations of substances based on time periods, which leads to the fact that in situ real-time measurement for multiple biomarkers remains a grand challenge. Here, we propose a wearable epidermal microfluidic patch with integrated microfluidic pumps and micro-valves for accelerated and continuous collection of the sweat, where the micro-pumps ensure the complete separation of old and new sweat for real-time detection of real concentration of biomarkers in sweat. The biomarker concentration at different time periods is detected by introducing a burst valve, which is used to assist in the analysis of the real-time detection. A quantitative relationship between the minimum burst pressure difference required for sequential collection and the size of the microchannel structure is established to overcome the effects of additional resistance at the gas-liquid interface. Additionally, the sensing modules, including sodium ion, chlorine ion, glucose, and pH level in sweat, are integrated into the patch to realize in situ, real-time detection of multiple biomarkers in the human sweat, decoding the correlation between changes in substance concentrations and physiological conditions. This work provides a unique and simplifying strategy for developing wearable sweat sensors for potential applications in health monitoring and disease diagnostics.

Epicatechin-assembled nanoparticles against renal ischemia/reperfusion injury.

Bioinspired and biosafety antioxidant nanoparticle assemblies from natural occurring molecules have been regarded as a class of effective therapeutic nanomaterials for addressing current inflammatory diseases such as acute kidney injury. In this study, a series of epicatechin-assembled nanoparticles have been developed via one-pot enzymatic polymerization of epicatechin. The prepared poly (epicatechin) (PEC) nanoparticles (NPs) showed excellent antioxidant capacity to scavenge multiple toxic free radicals, thus being able to effectively protect cells under oxidative stress conditions in vitro. Furthermore, in the renal ischemia/reperfusion model, blood renal function testing and renal tissue staining revealed a prominent therapeutic effect of PEC NPs. All these findings suggested that this class of bioinspired antioxidant nanoparticles provided a new therapeutic strategy for human ischemia/reperfusion-related diseases.

Robust Natural Polyphenolic Adhesives against Various Harsh Environments.

Although adhesive hydrogels have been extensively explored, the development of adhesives with long-term strong adhesion capacity under various harsh environments is still met with profound challenges such as sophisticated preparation, long-term curing, and low bonding strength. Herein, a series of robust adhesive hydrogels have been developed via the polyphenol-epoxy-cross-linking (PEC) reactions between natural polyphenols (extracts) and epoxy glycidyl ethers. The as-prepared natural polyphenolic adhesive hydrogels could induce strong adhesion onto several kinds of typical substrates (i.e., wood, glass, paper, PET, PMMA, and Fe) under both dry and wet conditions based on multi-interactions. Moreover, those natural polyphenolic adhesives exhibited good low-temperature and solvent resistance performances, which could be widely used in different kinds of device repairment (i.e., chemical, petroleum, wood, metal, glass, plastic, rubber, and other industries) under different conditions. This work could provide new opportunities toward natural-inspired robust adhesives in various fields ranging from chemical transportation, industrial manufacturing, architectural design, and marine engineering to daily life.

Smart Internal Bio-Glues.

Although smart bio-glues have been well documented, the development of internal bio-glues for non-invasive or minimally invasive surgery is still met with profound challenges such as safety risk and the lack of deep tissue penetration stimuli for internal usage. Herein, a series of smart internal bio-glues are developed via the integration of o-nitrobenzene modified biopolymers with up-conversion nanoparticles (UCNPs). Upon irradiation by near-infrared (NIR) light, the prepared smart bio-glues can undergo a gelation process, which may further induce strong adhesion between tissues under both dry and wet conditions based on multi-interactions. Moreover, those NIR light-responsive bio-glues with deeper tissue penetration ability demonstrate good biocompatibility, excellent hemostatic performance, and the potent ability to accelerate wound healing for both external and internal wounds. This work provides new opportunities for minimally invasive surgery, especially in internal wound healing using smart and robust bio-glues.

In Situ Regeneration of Silicon Microring Biosensors Coated with Parylene C.

Optical biosensors support disease diagnostic applications, offering high accuracy and sensitivity due to label-free detection and their optical resonance enhancement. However, optical biosensors based on noble metal nanoparticles and precise micro-electromechanical system technology are costly, which is an obstacle for their applications. Here, we proposed a biosensor reuse method with nanoscale parylene C film, taking the silicon-on-insulator microring resonator biosensor as an example. Parylene C can efficiently adsorb antibody by one-step modification without any surface treatment, which simplifies the antibody modification process of sensors. Parylene C (20 nm thick) was successfully coated on the surface of the microring to modify anti-carcinoembryonic antigen (anti-CEA) and specifically detect CEA. After sensing, parylene C was successfully removed without damaging the sensing surface for the sensor reusing. The experimental results demonstrate that the sensing response did not change significantly after the sensor was reused more than five times, which verifies the repeatability and reliability of the reusable method by using parylene C. This framework can potentially reduce the cost of biosensors and promote their further applications.

Research on Pt/NiOx/WO3-x:Ti/W Multijunction Memristors with Synaptic Learning and Memory Functions.

Artificial synapses based on biological synapses represent a new idea in the field of artificial intelligence with future applications. Current two-terminal RRAM devices have developed tremendously due to the adjustable synaptic plasticity of artificial synapses. However, these devices still have some problems, such as current leakage and poor durability. Here, we demonstrate a Pt/NiOx/WO3-x:Ti/W memristor with a pn-type heterojunction and two metal-semiconductor contacts, which exhibits good rectification. Due to the change in the internal potential barrier, the devices possess multiconductance states under different pulse modulations and memory characteristics, similar to synapses. The rectification characteristics of the device exhibit stable enhancement and suppression behavior. Each device in the 10 × 10 cross array we constructed can be written correctly, which verifies that leakage current does not appear in the device. The structure proposed in this work has great significance for the integration of large-scale memristor cross arrays.

Digital Programming Graphene Oxide Liquid Crystalline Hybrid Hydrogel by Shearing Microlithography.

Designing anisotropic architectures enables the creation of soft materials with rich properties and functions to artificially simulate the evolutionary diversity of biology. In the important liquid crystalline hybrid (LCH) hydrogels, free manipulation of liquid crystalline order in high accuracy and efficiency has been long pursued to design properties and functions but remains a challenge. Here, we realize digital programing LC order in graphene oxide LCH hydrogels in high size resolution (∼20 µm) and efficiency by using shearing microlithography. The localized shear-induced LC order organization is immobilized by cross-linking gelation, and we prepare graphene oxide LCH hydrogels with digital programmed patterns in a large area. The shearing order generates a vertical alignment of graphene oxide sheets in hydrogels and a considerable mechanical anisotropy controlled by the shearing angle and interval spacing. By diversely organizing geometry of LC order, the mechanical response behaviors of LCH hydrogels are designed to exhibit localized deformations, steered cracking, and programmable swelling actuations. Our work offers a versatile avenue to scalably digital program LCH hydrogels in a high efficiency and accuracy. The digital designed hydrogel promises wide uses in actuators, bioscaffolds, biomimetic materials, and soft designer materials.

Physical mechanism for the synapse behaviour of WTiOx-based memristors.

Tungsten-based memristors possess many advantages as candidates for memristive devices, including gradual changes in resistance states and memorization and learning functions. However, most previous reports mainly focus on studying synaptic learning rules instead of analysing the internal mechanism that results in the exterior learning rules. Herein, we discuss stacked Au/WTiOx/Au and Ti/WTiOx/Au devices in which the function of the resistance switch is realized by the externally induced local migration of oxygen ions. The consecutively adjustable multilevel resistance of the Au/WTiOx/Au device may be due to the variation in the barrier width and height in high oxygen vacancy concentrations. Additionally, the high and low resistance states of Ti/WTiOx/Au devices are considered as a result of the connection and rupture of the conductive filaments at low concentrations of oxygen vacancies. The physical mechanism construction and state-full synapse development through the control of ion migration provide insight into the applications of oxide-based memristors in neuromorphic computation.

A Linguistic Hierarchy Model with Self-Confidence Preference Relations and Its Application in Co-Regulation of Food Safety in China.

Linguistic preference relations are widely used by decision makers to elicit their preferences over alternatives in the Group Decision Making (GDM) process. Recent studies have shown that self-confidence, as an important human psychological behavior, has an important influence on decision-making results. However, multiple self-confidence levels of decision makers are seldom considered in the linguistic preference relation. Meanwhile many real-word decision-making problems are analyzed in a hierarchical structure, in which a complicated problem can be divided into several easier comprehended sub-problems. Hence, this paper aims at designing a linguistic hierarchy model with self-confidence preference relation (LHM-SCPR) to discuss complex GDM problems in a hierarchical structure. In the SC-LPR, each element contains two components, the first one is the preference value between pairs of alternatives, and the second one that is defined on a linguistic term set represents decision maker's self-confidence level associated to the first component. Meanwhile, a nonlinear programming model is proposed to derive individual preference vector from SC-LPR. Then, we apply LHM-SCPR in co-regulation of food safety to present the validity of this method, and find that improving the participation skills regarding co-regulation of food safety is the most pressing task. Finally, detailed comparative analysis and discussion are presented to verify the validity of the proposal.

An Optimization-Based Approach to Social Network Group Decision Making with an Application to Earthquake Shelter-Site Selection.

The social network has emerged as an essential component in group decision making (GDM) problems. Thus, this paper investigates the social network GDM (SNGDM) problem and assumes that decision makers offer their preferences utilizing additive preference relations (also called fuzzy preference relations). An optimization-based approach is devised to generate the weights of decision makers by combining two reliable resources: in-degree centrality indexes and consistency indexes. Based on the obtained weights of decision makers, the individual additive preference relations are aggregated into a collective additive preference relation. Further, the alternatives are ranked from best to worst according to the obtained collective additive preference relation. Moreover, earthquakes have occurred frequently around the world in recent years, causing great loss of life and property. Earthquake shelters offer safety, security, climate protection, and resistance to disease and ill health and are thus vital for disaster-affected people. Selection of a suitable site for locating shelters from potential alternatives is of critical importance, which can be seen as a GDM problem. When selecting a suitable earthquake shelter-site, the social trust relationships among disaster management experts should not be ignored. To this end, the proposed SNGDM model is applied to evaluate and select earthquake shelter-sites to show its effectiveness. In summary, this paper constructs a novel GDM framework by taking the social trust relationship into account, which can provide a scientific basis for public emergency management in the major disasters field.