Unraveling the Pathogenesis of Post-Stroke Depression in a Hemorrhagic Mouse Model through Frontal Lobe Circuitry and JAK-STAT Signaling.

Post-stroke depression is a common complication that imposes significant burdens and challenges on patients. The occurrence of depression is often associated with frontal lobe hemorrhage, however, current understanding of the underlying mechanisms remains limited. Here, the pathogenic mechanisms associated with the circuitry connectivity, electrophysiological alterations, and molecular characteristics are investigated related to the frontal lobe in adult male mice following unilateral injection of blood in the medial prefrontal cortex (mPFC). It is demonstrated that depression is a specific neurological complication in the unilateral hematoma model of the mPFC, and the ventral tegmental area (VTA) shows a higher percentage of connectivity disruption compared to the lateral habenula (LHb) and striatum (STR). Additionally, long-range projections originating from the frontal lobe demonstrate higher damage percentages within the connections between each region and the mPFC. mPFC neurons reveal reduced neuronal excitability and altered synaptic communication. Furthermore, transcriptomic analysis identifies the involvement of the Janus Kinase-Signal Transducer and Activator of Transcription (JAK-STAT) signaling pathway, and targeting the JAK-STAT pathway significantly alleviates the severity of depressive symptoms. These findings improve the understanding of post-hemorrhagic depression and may guide the development of efficient treatments.

A smart tablet-phone-based high-performance pancreatic cancer cell biosensing system for drug screening.

Exploring more efficient pancreatic cancer drug screening platforms is of significant importance for accelerating the drug development process. In this study, we developed a high-sensitivity bioluminescence system based on smartphones and smart tablets, and constructed a pancreatic cancer drug screening platform (PCDSP) by combining the pancreatic cancer cell sensing model (PCCSM) on the multiwell plates (MTP). A smart tablet was used as the light source and a smartphone as the colorimetric sensing device. The smartphone dynamically controls the color and brightness displayed on the smart tablet to achieve lower LOD and wider detection ranges. We constructed PCCSM for 24 h, 48 h, and 72 h , and performed colorimetric experiments using both PCDSP and a commercial plate reader (CPR). The results showed that the PCDSP had a lower LOD than that of CPR. Moreover, PCDSP even exhibited a lower LOD for 24 h PCCSM testing compared to CPR for 48 h PCCSM testing, effectively shortening the drug evaluation process. Additionally, the PCDSP offers higher portability and efficiency compared with CPR, making it a promising platform for efficient pancreatic cancer drug screening.

Two-Dimensional Deep Learning Frameworks for Drug-Induced Cardiotoxicity Detection.

The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.

Machine Learning Assisted Electronic/Ionic Skin Recognition of Thermal Stimuli and Mechanical Deformation for Soft Robots.

Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real-world applications. The development of electronic skin (E-skin) perception systems is crucial for the advancement of soft robots. However, achieving both exteroceptive and proprioceptive capabilities in E-skins, particularly in terms of decoupling and classifying sensing signals, remains a challenge. This study presents an E-skin with mixed electronic and ionic conductivity that can simultaneously achieve exteroceptive and proprioceptive, based on the resistance response of conductive hydrogels. It is integrated with soft robots to enable state perception, with the sensed signals further decoded using the machine learning model of decision trees and random forest algorithms. The results demonstrate that the newly developed hydrogel sensing system can accurately predict attitude changes in soft robots when subjected to varying degrees of pressing, hot pressing, bending, twisting, and stretching. These findings that multifunctional hydrogels combine with machine learning to decode signals may serve as a basis for improving the sensing capabilities of intelligent soft robots in future advancements.

Laser patterned graphene pressure sensor with adjustable sensitivity in an ultrawide response range.

Flexible pressure sensors have attracted wide attention because of their applications in wearable electronic, human-computer interface, and healthcare. However, it is still a challenge to design a pressure sensor with adjustable sensitivity in an ultrawide response range to satisfy the requirements of different application scenarios. Here, a laser patterned graphene pressure sensor (LPGPS) is proposed with adjustable sensitivity in an ultrawide response range based on the pre-stretched kirigami structure. Due to the out-of-plane deformation of the pre-stretched kirigami structure, the sensitivity can be easily tuned by simply modifying the pre-stretched level. As a result, it exhibits a maximum sensitivity of 0.243 kPa-1, an ultrawide range up to 1600 kPa, a low detection limit (6 Pa), a short response time (42 ms), and excellent stability with high pressure of 1200 kPa over 500 cycles. Benefiting from its high sensitivity and ultrawide response range, the proposed sensor can be applied to detect physiological and kinematic signals under different pressure intensities. Additionally, taking advantage of laser programmable patterning, it can be easily configured into an array to determine the pressure distribution. Therefore, LPGPS with adjustable sensitivity in an ultrawide response range has potential application in wearable electronic devices.

Micronano Synergetic Three-Dimensional Bioelectronics: A Revolutionary Breakthrough Platform for Cardiac Electrophysiology.

Cardiovascular diseases (CVDs) are the leading cause of mortality and therefore pose a significant threat to human health. Cardiac electrophysiology plays a crucial role in the investigation and treatment of CVDs, including arrhythmia. The long-term and accurate detection of electrophysiological activity in cardiomyocytes is essential for advancing cardiology and pharmacology. Regarding the electrophysiological study of cardiac cells, many micronano bioelectric devices and systems have been developed. Such bioelectronic devices possess unique geometric structures of electrodes that enhance quality of electrophysiological signal recording. Though planar multielectrode/multitransistors are widely used for simultaneous multichannel measurement of cell electrophysiological signals, their use for extracellular electrophysiological recording exhibits low signal strength and quality. However, the integration of three-dimensional (3D) multielectrode/multitransistor arrays that use advanced penetration strategies can achieve high-quality intracellular signal recording. This review provides an overview of the manufacturing, geometric structure, and penetration paradigms of 3D micronano devices, as well as their applications for precise drug screening and biomimetic disease modeling. Furthermore, this review also summarizes the current challenges and outlines future directions for the preparation and application of micronano bioelectronic devices, with an aim to promote the development of intracellular electrophysiological platforms and thereby meet the demands of emerging clinical applications.

High Temperature Oxidation Behavior of Additive Manufactured Ti6Al4V Alloy with the Addition of Yttrium Oxide Nanoparticles.

Titanium alloys face challenges of high temperature oxidation during the service period when used as aircraft engine components. In this paper, the effect of Y2O3 addition on the oxidation behavior and the microstructural change of the Ti6Al4V alloy fabricated by selective laser melting (SLM) was comprehensively studied. The results show that the surface of the Ti6Al4V alloy is a dense oxide layer composed of TiO2 and Al2O3 compounds. The thickness of the oxide layer of the Ti6Al4V increased from 59.55 µm to 139.15 µm. In contrast, with the addition of Y2O3, the thickness of the oxide layer increased from 35.73 µm to 80.34 µm. This indicates that the thickness of the oxide layer formation was a diffusion-controlled process and, therefore, the thickness of the oxide layer increased with an increase in temperature. The Ti6Al4V-1.0 wt.% Y2O3 alloy exhibits excellent oxidation resistance, and the thickness is significantly lower than that of the Ti6Al4V alloy. The oxidation kinetics of the Ti6Al4V and Ti6Al4V-1.0 wt.% Y2O3 alloys at 600 °C and 800 °C follows a parabolic rule, whereas the oxidation of the Ti6Al4V and Ti6Al4V-1.0 wt.% Y2O3 alloys at 1000 °C follows the linear law. The average microhardness values of Ti6Al4V samples after oxidation increased to 818.9 ± 20 HV0.5 with increasing temperature, and the average microhardness values of the Ti6Al4V-1.0 wt.% Y2O3 alloy increases until 800 °C and then decreases at 1000 °C. The addition of Y2O3 shows a significant improvement in the microhardness during the different temperatures after oxidation.

Soil enzyme profile analysis for indicating decomposer micro-food web.

Highly diverse exoenzymes mediate the energy flow from substrates to the multitrophic microbiota within the soil decomposer micro-food web. Here, we used a "soil enzyme profile analysis" approach to establish a series of enzyme profile indices; those indices were hypothesized to reflect micro-food web features. We systematically evaluated the shifts in enzyme profile indices in relation to the micro-food web features in the restoration of an abandoned cropland to a natural area. We found that enzymatic C:N stoichiometry and decomposability index were significantly associated with substrate availability. Furthermore, the higher Shannon diversity index in the exoenzyme profile, especially for the C-degrading hydrolase, corresponded to a greater microbiota community diversity. The increased complexity and stability of the exoenzyme network reflected similar changes with the micro-food web networks. In addition, the gross activity of the enzyme profile as a parameter for soil multifunctionality, effectively predicted the substrate content, microbiota community size, diversity, and network complexity. Ultimately, the proposed enzymic channel index was closely associated with the traditional decomposition channel indices derived from microorganisms and nematodes. Our results showed that soil enzyme profile analysis reflected very well the decomposer food web features. Our study has important implications for projecting future climate change or anthropogenic disturbance impacts on soil decomposer micro-food web features by using soil enzyme profile analysis.

Effect of Membrane Thickness on Ion Transport in pH-Regulated Zero-Depth Interfacial Nanopores.

Zero-depth interfacial nanopores, which are formed by two crossed nanoscale channels at their intersection interface, have been proposed to increase the spatial resolution of solid-state nanopores. However, research on zero-depth interfacial nanopores is still in its early stages. Although it has been shown that the current passing through an interfacial nanopore is largely independent of the membrane thickness, existing studies have not fully considered the impact of membrane thickness on other ion transport characteristics within these nanopores. In this paper, we investigate the electrokinetic ion transport phenomenon in the zero-depth interfacial nanopores, especially focusing on the influence of membrane thickness on the ion transport phenomenon. Our model incorporates the Poisson-Nernst-Planck equations and the Navier-Stokes equations, featuring a pH-regulated surface charge density. We find that when the thickness of the nanochannels is close to the interface size of the formed interfacial nanopore, the phenomenon of ion transport in the interfacial nanopore is similar to that in a conventional cylindrical nanopore. However, when the thickness of the nanochannels is much greater than the interface size of the formed interfacial nanopore, several distinct phenomena occur. The surface charge density on the inner walls of the interfacial nanopores has a small peak at the interface of the two crossing nanochannels, and the anion concentration changes greatly between the two nanochannels; that is, a much greater anion concentration forms in the nanochannel near the anode side than in the nanochannel near the cathode side. When the surface charge is nonzero, the electric field within the interfacial nanopore creates three extreme points, and the directions of the local electric fields are opposite at the ends of the membrane.

Electrokinetic Nanorod Translocation through a Dual-Nanopipette.

Glass nanopipettes, as important sensing tools, have attracted great interest due to their wide range of applications in detecting single molecules, nanoparticles, and cells. In this study, we investigated the translocation behavior of nanorod particles through dual-nanopipettes using a transient continuum-based model based on an arbitrary Lagrangian-Eulerian approach. Our findings indicate that the translocation of nanorods is slowed down in the dual-nanopipette system, especially in the dual-nanopipette system with a nanobridge. These results are in qualitative agreement with previous experimental findings reported in the literature. Additionally, the translocation of nanorods is influenced by factors such as bulk concentration, initial location of the nanorod, and surface charge of the nanopipette. Notably, when the surface charge density of the nanopipette is relatively high and the initial location of the nanorod is in the reservoir, the nanorod can hardly enter the nanopipette, resulting in a relatively low translocation efficiency. However, the translocation efficiency can be improved by initially positioning the nanorod in one of the barrels. The resulting dual-blockade current signal can be used to correlate the characteristics of the nanorod.

A new prognostic model for recurrent pregnancy loss: assessment of thyroid and thromboelastograph parameters.

Objective: This study aimed to identify predictors associated with thyroid function and thromboelastograph (TEG) examination parameters and establish a nomogram for predicting the risk of subsequent pregnancy loss in recurrent pregnancy loss (RPL). Methods: In this retrospective study, we analyzed the medical records of 575 RPL patients treated at Lanzhou University Second Hospital, China, between September 2020 and December 2022, as a training cohort. We also included 272 RPL patients from Ruian People's Hospital between January 2020 and July 2022 as external validation cohort. Predictors included pre-pregnancy thyroid function and TEG examination parameters. The study outcome was pregnancy loss before 24 weeks of gestation. Variable selection was performed using least absolute shrinkage and selection operator regression and stepwise regression analyses, and the prediction model was developed using multivariable logistic regression. The study evaluated the model's performance using the area under the curve (AUC), calibration curve, and decision curve analysis. Additionally, dynamic and static nomograms were constructed to provide a visual representation of the models. Results: The predictors used to develop the model were body mass index, previous pregnancy losses, triiodothyronine, free thyroxine, thyroid stimulating hormone, lysis at 30 minutes, and estimated percent lysis which were determined by the multivariable logistic regression with the minimum Akaike information criterion of 605.1. The model demonstrated good discrimination with an AUC of 0.767 (95%CI 0.725-0.808), and the Hosmer-Lemeshow test indicated good fitness of the predicting variables with a P value of 0.491. Identically, external validation confirmed that the model exhibited good performance with an AUC of 0.738. Moreover, the clinical decision curve showed a positive net benefit in the prediction model. Meanwhile, the web version we created was easy to use. The risk stratification indicated that high-risk patients with a risk score >147.9 had a higher chance of pregnancy loss (OR=6.05, 95%CI 4.09-8.97). Conclusions: This nomogram well-predicted the risk of future pregnancy loss in RPL and can be used by clinicians to identify high-risk patients and provide a reference for pregnancy management of RPL.

Unveiling the Hidden Impact: Hematoma Volumes Unravel Circuit Disruptions in Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) imposes a significant burden on patients, and the volume of hematoma plays a crucial role in determining the severity and prognosis of ICH. Although significant recent progress has been made in understanding the cellular and molecular mechanisms of surrounding brain tissue in ICH, our current knowledge regarding the precise impact of hematoma volumes on neural circuit damage remains limited. Here, using a viral tracing technique in a mouse model of striatum ICH, two distinct patterns of injury response were observed in upstream connectivity, characterized by both linear and nonlinear trends in specific brain areas. Notably, even low-volume hematomas had a substantial impact on downstream connectivity. Neurons in the striatum-ICH region exhibited heightened excitability, evidenced by electrophysiological measurements and changes in metabolic markers. Furthermore, a strong linear relationship (R2 = 0.91) was observed between hematoma volumes and NFL damage, suggesting a novel biochemical index for evaluating changes in neural injury. RNA sequencing analysis revealed the activation of the MAPK signaling pathway following hematoma, and the addition of MAPK inhibitor revealed a decrease in neuronal circuit damage, leading to alleviation of motor dysfunction in mice. Taken together, our study highlights the crucial role of hematoma size as a determinant of circuit injury in ICH. These findings have important implications for clinical evaluations and treatment strategies, offering opportunities for precise therapeutic approaches to mitigate the detrimental effects of ICH and improve patient outcomes.

Prospects and challenges for the application of tissue engineering technologies in the treatment of bone infections.

Osteomyelitis is a devastating disease caused by microbial infection in deep bone tissue. Its high recurrence rate and impaired restoration of bone deficiencies are major challenges in treatment. Microbes have evolved numerous mechanisms to effectively evade host intrinsic and adaptive immune attacks to persistently localize in the host, such as drug-resistant bacteria, biofilms, persister cells, intracellular bacteria, and small colony variants (SCVs). Moreover, microbial-mediated dysregulation of the bone immune microenvironment impedes the bone regeneration process, leading to impaired bone defect repair. Despite advances in surgical strategies and drug applications for the treatment of bone infections within the last decade, challenges remain in clinical management. The development and application of tissue engineering materials have provided new strategies for the treatment of bone infections, but a comprehensive review of their research progress is lacking. This review discusses the critical pathogenic mechanisms of microbes in the skeletal system and their immunomodulatory effects on bone regeneration, and highlights the prospects and challenges for the application of tissue engineering technologies in the treatment of bone infections. It will inform the development and translation of antimicrobial and bone repair tissue engineering materials for the management of bone infections.

Leptin attenuates the osteogenic induction potential of BMP9 by increasing ß-catenin malonylation modification via Sirt5 down-regulation.

BMP9 has demonstrated significant osteogenic potential. In this study, we investigated the effect of Leptin on BMP9-induced osteogenic differentiation. Firstly, we found Leptin was decreased during BMP9-induced osteogenic differentiation and serum Leptin concentrations were increased in the ovariectomized (OVX) rats. Both in vitro and in vivo, exogenous expression of Leptin inhibited the process of osteogenic differentiation, whereas silencing Leptin enhanced. Exogenous Leptin could increase the malonylation of ß-catenin. However, BMP9 could increase the level of Sirt5 and subsequently decrease the malonylation of ß-catenin; the BMP9-induced osteogenic differentiation was inhibited by silencing Sirt5. These data suggested that Leptin can inhibit the BMP9-induced osteogenic differentiation, which may be mediated through reducing the activity of Wnt/ß-catenin signalling via down-regulating Sirt5 to increase the malonylation level of ß-catenin partly.

Cyclic AMP signaling promotes regeneration of cochlear synapses after excitotoxic or noise trauma.

Introduction: Cochlear afferent synapses connecting inner hair cells to spiral ganglion neurons are susceptible to excitotoxic trauma on exposure to loud sound, resulting in a noise-induced cochlear synaptopathy (NICS). Here we assessed the ability of cyclic AMP-dependent protein kinase (PKA) signaling to promote cochlear synapse regeneration, inferred from its ability to promote axon regeneration in axotomized CNS neurons, another system refractory to regeneration. Methods: We mimicked NICS in vitro by applying a glutamate receptor agonist, kainic acid (KA) to organotypic cochlear explant cultures and experimentally manipulated cAMP signaling to determine whether PKA could promote synapse regeneration. We then delivered the cAMP phosphodiesterase inhibitor rolipram via implanted subcutaneous minipumps in noise-exposed CBA/CaJ mice to test the hypothesis that cAMP signaling could promote cochlear synapse regeneration in vivo. Results: We showed that the application of the cell membrane-permeable cAMP agonist 8-cpt-cAMP or the cAMP phosphodiesterase inhibitor rolipram promotes significant regeneration of synapses in vitro within twelve hours after their destruction by KA. This is independent of neurotrophin-3, which also promotes synapse regeneration. Moreover, of the two independent signaling effectors activated by cAMP - the cAMP Exchange Protein Activated by cAMP and the cAMP-dependent protein kinase - it is the latter that mediates synapse regeneration. Finally, we showed that systemic delivery of rolipram promotes synapse regeneration in vivo following NICS. Discussion: In vitro experiments show that cAMP signaling promotes synapse regeneration after excitotoxic destruction of cochlear synapses and does so via PKA signaling. The cAMP phosphodiesterase inhibitor rolipram promotes synapse regeneration in vivo in noise-exposed mice. Systemic administration of rolipram or similar compounds appears to provide a minimally invasive therapeutic approach to reversing synaptopathy post-noise.

Diagnostic and Predictive Efficacy of Synovial Fluid Versus Serum C-Reactive Protein Levels for Periprosthetic Joint Infection and Reimplantation Success.

BACKGROUND: A 2-stage exchange revision for periprosthetic joint infection (PJI) is associated with major risks for reinfection. Although serum markers are frequently used for diagnosis, their effectiveness remains debatable. Synovial fluid markers may offer a more accurate diagnosis of PJI; however, the importance of these biomarkers, notably synovial fluid C-reactive protein (syCRP), remains controversial, particularly in the context of reimplantation. The present study aimed to clarify these diagnostic uncertainties by evaluating the diagnostic efficacy of syCRP versus serum CRP (seCRP) levels in the context of PJI and recurring or persisting infections before reimplantation. METHODS: A total of 186 patients were enrolled and divided into 2 groups: aseptic revision (n = 112) and PJI revision (n = 74). Of the PJI group, 65 were categorized as success and 9 as failure, based on the presence of recurrent or persistent infection before reimplantation. The syCRP and seCRP levels and their changes were assessed preoperatively and in the first-stage and second-stage revisions. Additionally, receiver operating characteristic (ROC) curves and area under the ROC curves (AUCs) were analyzed. RESULTS: Both seCRP and syCRP levels were significantly elevated in the PJI group compared with the aseptic group (P < .001). The ROC curve analysis highlighted the enhanced diagnostic accuracy of syCRP for PJI, with an AUC of 0.93 versus 0.80 for seCRP. Furthermore, syCRP proved to be more reliable in predicting reimplantation success, exhibiting an AUC of 0.86 versus 0.63 for seCRP. In evaluating trends in CRP levels to determine reimplantation timing, changes in syCRP levels demonstrated superior diagnostic utility, exhibiting an AUC of 0.79 versus 0.63 for changes in seCRP levels. CONCLUSIONS: In assessing PJI and infections before reimplantation, syCRP may offer enhanced accuracy compared with seCRP. Nevertheless, variations in both syCRP and seCRP levels did not consistently predict the outcome of reimplantation.

An overview of current research on cancer stem cells: a bibliometric analysis.

BACKGROUND: Cancer stem cells (CSCs) represent a potential mechanism contributing to tumorigenesis, metastasis, recurrence, and drug resistance. The objective of this study is to investigate the status quo and advancements in CSC research utilizing bibliometric analysis. METHODS: Publications related to CSCs from 2010 to 2022 were collected from the Web of Science Core Collection database. Various analytical tools including CiteSpace, VOSviewer, Scimago Graphica, and GraphPad Prism were used to visualize aspects such as co-authorship, co-occurrence, and co-citation within CSC research to provide an objective depiction of the contemporary status and developmental trajectory of the CSC field. RESULTS: A total of 22,116 publications were included from 1942 journals written by 95,992 authors. Notably, China emerged as the country with the highest number of publications, whereas the United States exerted the most significant influence within the field. MD Anderson Cancer Center emerged as the institution making the most comprehensive contributions. Wicha M.S. emerged as the most prolific and influential researcher. Among journals, Cancers emerged as a focal point for CSC research, consistently publishing a wealth of high-quality papers. Furthermore, it was observed that most journals tended to approach CSC research from molecular, biological, and immunological perspectives. The research into CSCs encompassed a broad array of topics, including isolation and enrichment techniques, biomarkers, biological characteristics, cancer therapy strategies, and underlying biological regulatory mechanisms. Notably, exploration of the tumor microenvironment and extracellular vesicles emerged as burgeoning research frontiers for CSCs. CONCLUSION: The research on CSCs has garnered growing interest. A trend toward multidisciplinary homogeneity is emerging within the realm of CSCs. Further investigation could potentially center on the patients of extracellular vesicles and the tumor microenvironment in relation to CSCs.

Mechanism study on the influences of buffer osmotic pressure on microfluidic chip-based cell electrofusion.

Cell electrofusion is a key process in many research fields, such as genetics, immunology, and cross-breeding. The electrofusion efficiency is highly dependent on the buffer osmotic pressure properties. However, the mechanism by which the buffer osmotic pressure affects cell electrofusion has not been theoretically or numerically understood. In order to explore the mechanism, the microfluidic structure with paired arc micro-cavities was first evaluated based on the numerical analysis of the transmembrane potential and the electroporation induced on biological cells when the electrofusion was performed on this structure. Then, the numerical model was used to analyze the effect of three buffer osmotic pressures on the on-chip electrofusion in terms of membrane tension and cell size. Compared to hypertonic and isotonic buffers, hypotonic buffer not only increased the reversible electroporation area in the cell-cell contact zone by 1.7 times by inducing a higher membrane tension, but also significantly reduced the applied voltage required for cell electroporation by increasing the cell size. Finally, the microfluidic chip with arc micro-cavities was fabricated and tested for electrofusion of SP2/0 cells. The results showed that no cell fusion occurred in the hypertonic buffer. The fusion efficiency in the isotonic buffer was about 7%. In the hypotonic buffer, the fusion efficiency was about 60%, which was significantly higher compared to hypertonic and isotonic buffers. The experimental results were in good agreement with the numerical analysis results.

Controllable synthesis of a hybrid mesoporous sheets-like Fe0.5NiS2@ P, N-doped carbon electrocatalyst for alkaline oxygen evolution reaction.

Owing to the high cost of precious metal catalysts for the oxygen evolution reaction (OER), the production of highly efficient and affordable electrocatalysts is important for generating pollution-free and renewable energy via electrochemical processes. A facile hydrothermal approach was employed to synthesize hybrid mesoporous iron-nickel bimetallic sulfides @ P, N-doped carbon for the OER. The prepared Fe0.5NiS2@C exhibited an overpotential (η) of 250 mV at 10 mA/cm2. This exceeded the overpotentials recently reported for surface-modified P, N-doped carbon-based catalysts for the OER in a 1 M KOH medium. Moreover, the Fe0.5NiS2@C catalyst showed a notable Tafel slope of 90.5 mV/dec with long-dated stability even after 24 h at 10 mA/cm2. The superior OER performance of the Fe0.5NiS2@C catalysts may be due to their large surface area, sheet-like morphology with abundant active sites, fast transfer of mass and electrons, control of the electronic structure by co-treatment with heteroatoms (e.g., P and N), and the synergistic effect of bimetallic sulfides, making them favorable catalysts for the oxygen evolution reaction. Density functional theory (DFT) calculations showed that the Fe0.5NiS2@C catalyst exhibited strong H2O-adsorption energy. The enhanced OER activity of Fe0.5NiS2@C was attributed to its higher surface area, favorable H2O adsorption energy, improved electron transfer efficiency, and lower Gibbs free energy compared to those of the other proposed catalysts.