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The biomedical application of self-healing materials in wet or (under)water environments is quite challenging because the insulation and dissociation effects of water molecules significantly reduce the reconstruction of material–interface interactions. Rapid closure with uniform tension of high-tension wounds is often difficult, leading to further deterioration and scarring. Herein, a new type of thermosetting water-resistant self-healing bioelastomer (WRSHE) was designed by synergistically incorporating a stable polyglycerol sebacate (PGS) covalent crosslinking network and triple hybrid dynamic networks consisting of reversible disulfide metathesis (SS), and dimethylglyoxime urethane (Dou) and hydrogen bonds. And a resveratrol-loaded WRSHE (Res@WRSHE) was developed by a swelling, absorption, and crosslinked network locking strategy. WRSHEs exhibited skin-like mechanical properties in terms of nonlinear modulus behavior, biomimetic softness, high stretchability, and good elasticity, and they also achieved ultrafast and highly efficient self-healing in various liquid environments. For wound-healing applications of high-tension full-thickness skin defects, the convenient surface assembly by self-healing of WRSHEs provides uniform contraction stress to facilitate tight closure. Moreover, Res@WRSHEs gradually release resveratrol, which helps inflammatory response reduction, promotes blood vessel regeneration, and accelerates wound repair.

Despite neuromorphic computing (NC) technologies offer tremendous potential in executing computationally intensive tasks with high efficiency and low latency, most of existing methods are still difficult to achieve software-comparable accuracy. To address this challenge, we develop a multimodal local-global NC system (MLG-NCS) that can capture local characteristics and exchange global cross-modal information sufficiently. Specifically, a high-density memristor crossbar array is prepared to perform efficient parallel in-memory operations, serving as the fundamental component of the proposed MLG-NCS. To facilitate understanding of the proposed MLG-NCS design, the local feature representation module, the global cross-modal interaction module, and the output module are designed. The experimental results show that the proposed system has advantages in classification accuracy (ranked top three), time consumption (approximately ten times speed up), and latency (about 1.2–15.3 times faster), enabling good inter-related tradeoffs between latency, efficiency, and accuracy. This study is expected to promote the revolution and development of next-generation computing system, which takes a firm step toward artificial general intelligence (AGI).

Adverse weather conditions severely impact the environmental perception capabilities of the Advanced Driver Assistance System (ADAS). However, it is difficult to deploy these high-performance Transformers for image restoration on resources constrained ADAS platform. Recently, the rapid development of neuromorphic computing systems (NSC), based on fast in-memory computing, provides an ideal and feasible solution to it. Combining Transformers and NSC faces challenges due to the high complexity and outliers of Softmax attention. Moreover, existing image restoration networks are mostly designed for a single task and lack generalization. To address these issues, we propose a neuromorphic-computing-friendly Linear Sparse Transformer (LSFormer) with linear computational complexity. The LSFormer achieves advanced image restoration performance compared to Softmax attention Transformers. Meanwhile, we propose the implementation scheme based on neuromorphic computing systems, reducing the deployment complexity at the edge. We evaluate LSFormer on multiple image restoration tasks including low-light image enhancement, image deraining, and dehazing. The results demonstrates that the performance of LSFormer outperforms state-of-the-art (SOTA) networks. Furthermore, the comparison between existing hardware implementations of Softmax attention and our proposed scheme using various metrics validates the feasibility and superiority of our proposed scheme.

Currently, 3D-bioprinting technique has emerged as a promising strategy to offer native-like tracheal substitutes for segmental trachea reconstruction. However, there has been very limited breakthrough in tracheal repair using 3D-bioprinted biomimetic trachea owing to the lack of ideal bioinks, the requirement for precise structural biomimicking, and the complexity of multi-step surgical procedures by mean of intramuscular pre-implantation. Herein, we propose a one-step surgical technique, namely direct end-to-end anastomosis using C-shape 3D-bioprinted biomimetic trachea, for segmental trachea defect repair. First, two types of tissue-specific matrix hydrogels were exploited to provide mechanical and biological microenvironment conducive to the specific growth ways of cartilage and fibrous tissue respectively. In contrast to our previous O-shape tracheal design, the tubular structure of alternating C-shape cartilage rings and connecting vascularized-fibrous-tissue rings was meticulously designed for rapid 3D-bioprinting of tracheal constructs with optimal printing paths and models. Furthermore, in vivotrachea regeneration in nude mice showed satisfactory mechanical adaptability and efficient physiological regeneration. Finally, in situsegmental trachea reconstruction by direct end-to-end anastomosis in rabbits was successfully achieved using 3D-bioprinted C-shape biomimetic trachea. This study demonstrates the potential of advanced 3D-bioprinting for instant and efficient repair of segmental trachea defects.

The human visual system can recognize objects and process visual information in complex environments, which inspires us to develop neuromorphic visual systems through electronic devices. However, artificial visual systems face severe processing efficiency and power consumption challenges due to the separation of the sensors, the storage unit, and the processing unit. This article proposes a memristive-optoelectronic sensor for neuromorphic visual systems. It has the function of simultaneously sensing light signals and storing and processing information. First, an Ag/GQDs/TiOx/FTO memristor with a negative photoconductance (NPC) effect is prepared. Its performance tests show that the resistance value of this memristor increases in the high-resistance state (HRS) branch under illumination, and we explore its internal dynamic principles in detail. An optoelectronic memristor model is constructed, which can simulate the real states of the memristor with satisfactory fitting accuracy (greater than 99.82%). A series of simulation experiments are made based on the model to analyze its characteristics. Furthermore, a memristor-based sensing-memory-computing neuromorphic visual system is designed, which consists of an artificial visual array and a memristor crossbar array. Compared with conventional artificial visual systems based on the Von Neumann computing architecture, our designed neuromorphic visual system can simplify the circuit, reduce system power consumption, and improve information processing efficiency. It shows great potential for applications in neuromorphic visual systems and contributes to the development of visual edge computing.

Immunoregulatory and vascularized microenvironments play an important role in bone regeneration; however, the precise regulation for vascularization and inflammatory reactions remains elusive during bone repair. In this study, by means of subcutaneous preimplantation, we successfully constructed demineralized bone matrix (DBM) grafts with immunoregulatory and vascularized microenvironments. According to the current results, at the early time points (days 1 and 3), subcutaneously implanted DBM grafts recruited a large number of pro-inflammatory M1 macrophages with positive expression of CD68 and iNOS, while at the later time points (days 7 and 14), these inflammatory cells gradually subsided, accompanying increased presence of anti-inflammatory M2 macrophages with positive expression of CD206 and Arg-1, indicating a gradually enhanced anti-inflammatory microenvironment. At the same time, the gradually increased angiogenesis was observed in the DBM grafts with implantation time. In addition, the positive cells of CD105, CD73, and CD90 were observed in the inner region of the DBM grafts, implying the homing of mesenchymal stem cells. The repair results of cranial bone defects in a rat model further confirmed that the subcutaneous DBM xenografts at 7 days significantly improved bone regeneration. In summary, we developed a simple and novel strategy for bone regeneration mediated by anti-inflammatory microenvironment, prevascularization, and endogenous stem cell homing.

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

With the rapid development of edge devices, the deployment of AI on these devices has become a focal point for researchers. Moreover, the emergence of multimodal neural networks has facilitated the development and application of brain-inspired cognitive and recognition systems. However, the limited storage and computing resources of edge devices, along with the robustness issues of computational circuits, pose significant challenges in implementing AI systems with brain-inspired recognition capabilities on edge devices. To address this, we propose a memristor-based brain-inspired recognition (MBR) system, which can mimic the brain’s information processing mechanism without additional cross-modal processing, making its behavior more akin to human responses. In addition, the proposed MBR system is implemented using the multimodal in-memory computing (IMC) framework and validated the robustness and effectiveness of the proposed system through simulation analyzing. Furthermore, the multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) tasks are implemented on the MBR system with only one single training process. The results demonstrate the proposed system achieves superior performance compared to most existing baseline methods. Lastly, since the MBR system relies on memristor-based matrix multiplication, it emerges as one of the promising solutions for edge-based brain-inspired recognition applications.

Mental health problems are an increasingly common social issue severely affecting health and well-being. Multimedia processing technologies via facial expression show appealing prospects in the consumer field for mental health monitoring, while still suffer from intensive computation and low energy efficiency. This paper proposes an energy-efficiency memristive sequencer network (EMSN) for human emotion classification, which offers an environmentally friendly approach for consumers with low cost and easily deployable hardware. Firstly, two-dimensional (2D) materials are employed to construct an eco-friendly memristor, the efficacy and reliability of which are confirmed through performance testing. Then, a sequencer block is proposed using memristive circuits. Notably, it is a core component of the EMSN, consisting of a bidirectional long short-term memory circuit, normalisation circuit module, and multi-layer perception module. After combining some necessary function modules, the EMSN can be achieved. Furthermore, the proposed EMSN is applied for human emotion classification. The experimental results demonstrate that the proposed EMSN has advantages in computational efficiency and classification accuracy compared to existing mainstream methods, indicating an advancement in consumer health monitoring.

The eco-friendly and easily available glucose (Gl) is adopted as the interface modifier of the indium tin oxide (ITO)/perovskite film. Gl improved the interface contact between ITO and perovskite film while simultaneously passivating interface defects. A satisfactory 21.08% of PCE is obtained with a high open-circuit voltage and enhanced shelf-life stability.

Human decalcified bone matrix (HDBM) is a framework with a porous structure and good biocompatibility. Nevertheless, its oversized pores lead to massive cell loss when seeding chondrocytes directly over it. Gelatin (GT) is a type of protein obtained by partial hydrolysis of collagen. The GT scaffold can be prepared from the GT solution through freeze-drying. More importantly, the pore size of the GT scaffold can be controlled by optimizing the concentration of the GT solution. Similarly, when different concentrations of gelatin are combined with HDBM and then freeze-dried, the pore size of the HDBM can be modified to different degrees. In this study, the HDBM framework was modified with 0.3, 0.6, and 0.9%GT, resulting in an improved pore size and adhesion rate. Results showed that the HDBM framework with 0.6%GT (HDBM-0.6%GT) had an average pore size of 200 μm, which was more suitable for chondrocyte seeding. Additionally, our study validated that porcine decalcified bone matrix (PDBM) had a proper pore structure. Chondrocytes were in vitro seeded on the three frameworks for 4 weeks and then implanted in nude mice and autologous goats, respectively. The in vivo cartilage regeneration results showed that HDBM-0.6%GT and PDBM frameworks compensated for the oversized pores of the HDBM framework. Moreover, they showed successfully regenerated more mature cartilage tissue with a certain shape in animals.

Despite numerous studies on tissue-engineered injectable cartilage, it is still difficult to realize stable cartilage formation in preclinical large animal models because of suboptimal biocompatibility, which hinders further application in clinical settings. In this study, we proposed a novel concept of cartilage regeneration units (CRUs) based on hydrogel microcarriers for injectable cartilage regeneration in goats. To achieve this goal, hyaluronic acid (HA) was chosen as the microparticle to integrate gelatin (GT) chemical modification and a freeze-drying technology to create biocompatible and biodegradable HA-GT microcarriers with suitable mechanical strength, uniform particle size, a high swelling ratio, and cell adhesive ability. CRUs were then prepared by seeding goat autologous chondrocytes on the HA-GT microcarriers and culturing in vitro. Compared with traditional injectable cartilage methods, the proposed method forms relatively mature cartilage microtissue in vitroand improves the utilization rate of the culture space to facilitate nutrient exchange, which is necessary for mature and stable cartilage regeneration. Finally, these precultured CRUs were used to successfully regenerate mature cartilage in nude mice and in the nasal dorsum of autologous goats for cartilage filling. This study provides support for the future clinical application of injectable cartilage.

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

We present a circuit design of the hierarchical attention network for multimodal affective computing, which can be used in mental health monitoring. Specifically, a kind of cost-effective memristor is fabricated using the albumen protein, and the corresponding testing performance is conducted to ensure its efficiency and stability. Then, considering the hierarchical mechanism inspired by the human limbic system, the nanoscale memristors arranged in a crossbar array configuration are further applied to construct a compact hierarchical attention network that can perform the multimodal affective computing. Furthermore, based on the wearable technology and flexible electronics technology, a mental health monitoring system with low privacy invasiveness, low energy consumption, and low fabrication cost can be designed. Based on the mapping relationship between the multimodal affective computing and mental health, the mental health state of the current user can be monitored. This study is expected to help achieving the deep integration of neuromorphic electronics and mental health monitoring system, further promoting the development of next-generation consumer healthcare technology in smart city.

To enable smart home applications, embedding home monitoring systems with different sensors can be a feasible remedy to capture the multimodal sensory information from daily life. However, the previously developed monitoring systems have high implementation cost, large power consumption, and complicated device configuration, which are not conducive to achieving carbon neutrality and carbon peak goals. Here, we demonstrate a multimodal neuromorphic sensory-processing system with memristor circuits for smart homes, offering a more environmentally friendly approach with low cost and easily deployable hardware. We used three components to facilitate the multimodal neuromorphic sensory-processing system design. First, a memristor crossbar array is fabricated using low-cost, reliable, and eco-friendly 2-D materials; enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. Second, a neuromorphic multimodal sensory-processing module is designed for collecting and processing multiple sensory cues efficiently; to establish accurate depiction of the environment and realize timely response. Third, the biomimetic hierarchical learning module is realized for smart home monitoring and relative applications (e.g., indoor human behavior recognition). The developed multimodal neuromorphic sensory-processing system effectively combines the nanotechnology, intelligent sensing, and deep learning; indicating an advancement in smart home applications.

Abdominal adhesions, a commonly observed complication of abdominal surgery, have a high incidence and adversely affect patients’ physical and mental health. The primary causes of abdominal adhesions are intraoperative trauma, acute inflammatory response, bleeding, and foreign body infection. Because most current treatment approaches for abdominal adhesions are limited, improved and novel postoperative anti-adhesion regimens are urgently needed. In this study, we developed calcium polyphenol network (CaPN) microspheres based on the self-assembly of the natural triphenolic compound gallic acid and Ca2+in solution. The physicochemical properties of CaPNs, including their hemostatic, antibacterial, antioxidant, and anti-inflammatory activities, were investigated in vitro. Bleeding and cecal-abdominal wall adhesion models were established to observe the hemostatic activity of CaPNs and their preventive effect on postoperative abdominal wall adhesion in vivo. The results showed that CaPNs significantly reduced inflammation, oxidative stress, fibrosis, and abdominal adhesion formation and had good hemostatic and antibacterial properties. Our findings suggest a novel strategy for the prevention of postoperative adhesions.

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Memristive technologies are attractive due to their non-volatility, high-density, low-power and compatibility with CMOS. For memristive devices, a model corresponding to practical behavioral characteristics is highly favorable for the realization of its neuromorphic system and applications. This paper presents a novel flexible memristor model with electronic resistive switching memory behavior. Firstly, the Ag-Au $/$ MoSe2-doped Se $/$ Au-Ag memristor is prepared using hydrothermal synthesis method and magnetron sputtering method, and its performance test is conducted on an electrochemical workstation. Then, the mathematical model and SPICE circuit model of the Ag-Au $/$ MoSe2-doped Se $/$ Au-Ag memristor are constructed. The model accuracy is verified by using the electrochemical data derived from the performance test. Furthermore, the proposed model is applied to the circuit implementation of spiking neural network with biological mechanism. Finally, computer simulations and analysis are carried out to verify the validity and effectiveness of the entire scheme.

Tissue engineering provides a promising strategy for auricular reconstruction. Although the first international clinical breakthrough of tissue-engineered auricular reconstruction has been realized based on polymer scaffolds, this approach has not been recognized as a clinically available treatment because of its unsatisfactory clinical efficacy. This is mainly since reconstruction constructs easily cause inflammation and deformation. In this study, we present a novel strategy for the development of biological auricle equivalents with precise shapes, low immunogenicity, and excellent mechanics using auricular chondrocytes and a bioactive bioink based on biomimetic microporous methacrylate-modified acellular cartilage matrix (ACMMA) with the assistance of gelatin methacrylate (GelMA), poly(ethylene oxide) (PEO), and polycaprolactone (PCL) by integrating multi-nozzle bioprinting technology. Photocrosslinkable ACMMA is used to emulate the intricacy of the cartilage-specific microenvironment for active cellular behavior, while GelMA, PEO, and PCL are used to balance printability and physical properties for precise structural stability, form the microporous structure for unhindered nutrient exchange, and provide mechanical support for higher shape fidelity, respectively. Finally, mature auricular cartilage-like tissues with high morphological fidelity, excellent elasticity, abundant cartilage lacunae, and cartilage-specific ECM deposition are successfully regenerated in vivo, which provides new opportunities and novel strategies for the fabrication and regeneration of patient-specific auricular cartilage.

For organic memristors, non-zero-crossing current–voltage (I–V) curves are often observed, which can be attributed to capacitive effects. If the conversion between the capacitance-dominated state (CDS) and the memristance-dominated state (MDS) can be realized in a controllable manner, more device functions can be obtained. In this work, a two-terminal memristor using a common organic dye, azobenzene (AZB), as the active layer was prepared. It is found that as the applied voltage gradually increases, the device can transition from CDS to MDS. In the low voltage range (<1 V), the device is in CDS, and the capacitance is significantly increased by ∼104compared to the theoretical value. In the high voltage range (>1 V), the device is in MDS, achieving an HRS (high resistance state)/LRS (low resistance state) resistance ratio of ∼104, and the logic operations are achieved. Through the analysis of the I–Vcurve, energy diagram of the materials, and computer simulation results, the mechanisms of CDS, MDS, and their conversion are proposed. This work provides an in-depth understanding of the working mechanism of organic memristors and demonstrates the potential of AZB-based organic memristors for information storage and logic display applications.

Tissue-engineered cartilage regeneration by bone marrow stromal cells (BMSCs) is considered an ideal method. However, how to regulate BMSCs to regenerate specific types of cartilage remains unclear, which significantly limits its clinical translation and leads to suboptimal clinical effects. Herein, we systematically explored the role of native ear and articular cartilage niches on the differentiation fate of BMSCs and the type of regenerated cartilage. First, we prepared two types of acellular cartilage sheets (ACSs) and two types of chondrocytes. Then green fluorescent protein-labeled BMSCs were seeded on two types of ACSs with or without corresponding types of chondrocytes using a sandwich model and directed or cross-implanted them into native cartilage niches. After one year of in vivoculture, cell tracking and the results of histological results showed that the native cartilage niches were capable of regulating BMSCs regeneration into specific types of cartilage that were consistent with the cartilage types of the implanted sites. Furthermore, even when the type of niche formed by ACSs or the biomimetic cartilage niche constructed by specific types of ACSs and specific types of chondrocytes did not match with the native cartilage niche, the native cartilage niche continued to determine the type of cartilage regenerated by implanted BMSCs and chondrocytes. All our results provide sufficient evidence for specific types of cartilage regeneration using chondrogenic potential cells, such as mesenchymal stem cells and chondrocytes.

Sulfate-reducing bacteria (SRB), separated from Daqing oilfield wastewater, were used to prepare biological iron sulfides (SRB-Iron Sulfides). Biological iron sulfides are the biological combination of SRB and various iron sulfides mainly composed of mackinawite (FeS). Iron sulfides could be extracted by mechanically grinding the mixture SRB-Iron Sulfides. It is found that alkyl chains of sodium alkylbenzene sulfonate (ABS) can bind to the iron sulfides particles by van der Waals force to form modified particles (ABS-SRB-Iron Sulfides and ABS-Iron Sulfides). After modification, surface properties and dispersing properties have changed. In this study, iron sulfides (biosynthesized, mechanically grinding, ABS coated or not) were analyzed with X-ray diffraction patterns (XRD) and scanning electron microscopy (SEM). Besides, the effect of iron sulfides on oil–water dynamic interfacial tensions (DIFTs) was investigated. The results show that SRB-Iron Sulfides and Iron Sulfides presented hydrophilic and lipophilic properties to some extent, respectively. Excellent aqueous phase dispersion of the modified particles can be attributed to the presence of electrostatic repulsion and steric hindrance. The results showed that iron sulfides had a slight effect on DIFTs and the DIFTs curves display the “L” shape. For Iron Sulfides, the cooperativity effect with ABS is intensive, and a more stable and dense adsorption layer can be formed, leading to the lower IFT.

With the rapid development of mobile internet and artificial intelligence, wearable electronic devices have a great market prospect. In particular, information storage and processing of real-time collected data are an indispensable part of wearable electronic devices. Biomaterial-based memristive systems are suitable for storage and processing of the obtained information in wearable electronics due to the accompanying merits, i.e.sustainability, lightweight, degradability, low power consumption, flexibility and biocompatibility. So far, many biomaterial-based flexible and wearable memristive devices were prepared by spin coating or other technologies on a flexible substrate at room temperature. However, mechanical deformation caused by mechanical mismatch between devices and soft tissues leads to the instability of device performance. From the current research and practical application, the device will face great challenges when adapting to different working environments. In fact, some interesting studies have been performed to address the above issues while they were not intensively highlighted and overviewed. Herein, the progress in wearable biomemristive devices is reviewed, and the outlook and perspectives are provided in consideration of the existing challenges during the development of wearable biomemristive systems.

In this work, calcium nitrate and sodium silicate were employed as the sources of calcium and silicon, respectively, to synthesize sheet calcium silicate hydrate (CSH) by simple chemical precipitation. The obtained sheet CSH exhibited fast adsorption rate and large adsorption capacity, the maximum adsorption capacity is 556.3 mg g−1, for Cu(II) ions. The CSH after adsorption of Cu(II) ions (CSH-Cu) was characterized by XRD, FTIR, SEM, N2adsorption and XPS, and which could be converted into a catalyst for rhodamine B (RhB) degradation by a simple high-temperature activation process. The results showed that the catalyst give the excellent reusability, and after four cycles of the catalyst, more than 90% of the RhB could still be degraded within 240 min. The free radical trapping experiments showed that all of the ·OH, ·O2−and h+generated on the surface of the catalyst played an essential role in the degradation process, while h+made the largest contribution. Based on this, the degradation mechanism of RhB is also proposed.

Interface shear failure of composite concrete structures highlights the importance of shear performance research. Existing research results indicate that surface roughness plays a pivotal role on the shear behaviour of the old and new concrete interface. In several studies, interfaces with random roughness tooth geometries have been utilised, while, researches considering regular and defined tooth geometries are very rare. In this paper, an experimental and numerical investigation of the influence of roughness tooth geometrical parameters on the shear behaviour of an old and new concrete interface, considering a roughness tooth, was presented. This serves as a proof of concept in the use of quantitatively controlled roughness surface geometry for improved interface shear performance. Using the tooth angle, tooth depth, and the external normal pressure as test parameters, eighteen double-sided shear test specimens were prepared and tested till failure. In addition, numerical parametric studies on the roughness tooth angle and tooth depth for enhanced interface shear behaviour were conducted. Obtained results show the significant effect of roughness tooth angle and tooth depth on the shear capacity of the interface. Using a roughness tooth angle of about 20° approved to produce the highest interface shear capacity among all tested roughness angles. Also, increased roughness tooth depth enhanced interface shear capacity. A 10 mm increase in the tooth depth increased the ultimate shear load by a significant 47%. Furthermore, the external pressure significantly increases the shear capacity of the interface, with the positive influence of the initial external pressure more obvious at lower external pressure values. Increasing the initial external pressure from 1.0 to 1.5 MPa and from 1.5 to 2.0 MPa improved the interface ultimate shear capacity by 16.3% and 12.3%, respectively.

Drug resistance remains the dominant impediment for cancer therapy, not only because compensatory drug resistance pathways are always activated, but also because of the cross-resistance of cancer cells to unrelated therapeutics. Herein, chemodrug-sensitive cancer cells, intrinsic drug-resistant cells, and acquired resistant cells were employed to uncover their biological response to a nanoparticle-based photodynamic method in tumoral, cellular, and molecular levels. We observed that nanoparticle-based photodynamic process with high therapeutic efficiency, intracellular delivery, and tumor penetration effect resulted in the indiscriminate and significant therapeutic outcome, in contrast to the diversiform effect of first-line chemo-drug, Temozolomide (TMZ). By real-time quantitative PCR array technique, we revealed that signals in classical resistance pathways were unaffected or downregulated, and photodynamic effect initiates cell apoptosis via downstream genes. The discovery that nanoparticulate photodynamic therapy bypasses the signals in multiple resistant pathways may imply an alternative route for combating drug resistance of cancer.