Emulation and evaluation of tumor cell combined chemotherapy in isotropic/anisotropic collagen fiber microenvironments.

The rapid emergence of anisotropic collagen fibers in the tissue microenvironment is a critical transition point in late-stage breast cancer. Specifically, the fiber orientation facilitates the likelihood of high-speed tumor cell invasion and metastasis, which pose lethal threats to patients. Thus, based on this transition point, one key issue is how to determine and evaluate efficient combination chemotherapy treatments in late-stage cancer. In this study, we designed a collagen microarray chip containing 241 high-throughput microchambers with embedded metastatic breast cancer cell MDA-MB-231-RFP. By utilizing collagen's unique structure and hydromechanical properties, the chip constructed three-dimensional isotropic and anisotropic collagen fiber structures to emulate the tumor cell microenvironment at early and late stages. We injected different chemotherapeutic drugs into its four channels and obtained composite biochemical concentration profiles. Our results demonstrate that anisotropic collagen fibers promote cell proliferation and migration more than isotropic collagen fibers, suggesting that the geometric arrangement of fibers plays an important role in regulating cell behavior. Moreover, the presence of anisotropic collagen fibers may be a potential factor leading to the poor efficacy of combined chemotherapy in late-stage breast cancer. We investigated the efficacy of various chemotherapy drugs using cell proliferation inhibitors paclitaxel and gemcitabine and tumor cell migration inhibitors 7rh and PP2. To ensure the validity of our findings, we followed a systematic approach that involved testing the inhibitory effects of these drugs. According to our results, the drug combinations' effectiveness could be ordered as follows: paclitaxel + gemcitabine > gemcitabine + 7rh > PP2 + paclitaxel > 7rh + PP2. This study shows that the biomimetic chip system not only facilitates the creation of a realistic in vitro model for examining the cell migration mechanism in late-stage breast cancer but also has the potential to function as an effective tool for future chemotherapy assessment and personalized medicine.

How can multiscenario flow paths of water supply services be simulated? A supply-flow-demand model of ecosystem services across a typical basin in China.

Ecosystems provide many benefits to humans, and among them, water supply is crucial for human survival and development. This research focused on the Yangtze River Basin as the research area, quantitatively evaluated the temporal-spatial dynamic changes in the supply and demand of water supply services and determined the spatial relationship between the supply and demand regions of water supply services. We constructed the supply-flow-demand model of water supply service to quantify its flow. In our research, the Bayesian model was used to establish a multiscenario model of the water supply service flow path to simulate it and clarify its spatial flow path, flow direction and flow magnitude from the supply region to the demand region and determine its changing characteristics and driving factors in the basin. The results show that (1) In 2010, 2015 and 2020, the amount of water supply services showed a decreasing trend and was approximately 133.57 × 1012 m3, 129.97 × 1012 m3 and 120.82 × 1012 m3, respectively. (2) From 2010 to 2020, the trend of the cumulative flow of water supply service flow decreased each year and was 59.814 × 1012 m3, 56.930 × 1012 m3, 56.325 × 1012 m3 respectively. (3) Under the multiscenario simulation, the flow path of the water supply service was generally the same. The proportion of the water supply region was the highest under the green environmental protection scenario, at 73.8 %, and the proportion of the water demand region was the highest under the economic development and social progress scenario, at 27.3 %. (4) The provinces and municipalities in the basin were divided into three types of regions according to the matching relationship between supply and demand: catchment region, flow pass-through region and outflow region. The number of outflow regions was lowest, accounting for 23.53 %% of the regions, while the number of flow pass-through regions was the highest, accounting for 52.94 %.

Ultrastiff Amyloid-Fibril Network of α-Synuclein Formed by Surface Seeding Reaction Confirmed by Multichannel Electrodeless Quartz-Crystal-Microbalance Biosensor.

We developed a multichannel wireless quartz-crystal-microbalance (QCM) biosensor for mechanically studying the on-surface aggregation reaction of α-synuclein (α-syn). We find a quite unusual change in the resonant frequency that eventually exceeds the baseline, which has never been observed during seeding aggregation reaction. By incorporating a growth-to-percolation theory for fibril elongation reaction, we have favorably reproduced this unusual response and found that it can be explained only with formation of an ultrastiff fibril network. We also find that the stiffness of the fibril network grown from artificially prepared twist-type seeds is significantly higher than that from rod-type seeds. Furthermore, the stiffnesses of fibril networks grown from seeds derived from brain tissues of Parkinson's disease (PD) and multiple system atrophy (MSA) patients show a very similar trend to those of rod and twist seeds, respectively, indicating that fibrils from MSA patients are stiffer than those from PD.

Mass-Fabrication Scheme of Highly Sensitive Wireless Electrodeless MEMS QCM Biosensor with Antennas on Inner Walls of Microchannel.

Quartz-crystal-microbalance (QCM) biosensor is a typical label-free biosensor, and its sensitivity can be greatly improved by removing electrodes and wires that would be otherwise attached to the surfaces of the quartz resonator. The wireless-electrodeless QCM biosensor was then developed using a microelectro-mechanical systems (MEMS) process, although challenges remain in the sensitivity, the coupling efficiency, and the miniaturization (or mass production). In this study, we establish a MEMS process to obtain a large number of identical ultrasensitive and highly efficient sensor chips with dimensions of 6 mm square. The fundamental shear resonance frequency of the thinned AT-cut quartz resonator packaged in the microchannel exceeds 160 MHz, which is excited by antennas deposited on inner walls of the microchannel, significantly improving the electro-mechanical coupling efficiency in the wireless operation. The high sensitivity of the developed MEMS QCM biosensors is confirmed by the immunoglobulin G (IgG) detection using protein A and ZZ-tag displaying a bionanocapsule (ZZ-BNC), where we find that the ZZ-BNC can provide more effective binding sites and higher affinity to the target molecules, indicating a further enhancement in the sensitivity of the MEMS QCM biosensor. We then perform the label-free C-reactive protein (CRP) detection using the ZZ-BNC-functionalized MEMS QCM biosensor, which achieves a detection limit of 1 ng mL-1 or less even with direct detection.

3D collagen microchamber arrays for combined chemotherapy effect evaluation on cancer cell numbers and migration.

Breast cancer metastasis involves complex mechanisms, particularly when patients are undergoing chemotherapy. In tissues, tumor cells encounter cell-cell interactions, cell-microenvironment interactions, complex nutrient, and drug gradients. Currently, two-dimensional cell culture systems and animal models are challenging to observe and analyze cell responses to microenvironments with various physical and bio-chemical conditions, and microfluidic technology has been systematically developed to address this dilemma. In this study, we have constructed a combined chemotherapy evaluation chip (CCEC) based on microfluidic technology. The chip possesses 192 diamond-shaped microchambers containing MDA-MB-231-RFP cells, and each microchamber is composed of collagen to mimic breast cancer and its surrounding microenvironment. In addition, by adding medium containing different drugs to the medium channels of CCEC, composite drug (paclitaxel+gemcitabine+7rh and paclitaxel+fluorouracil+PP2) concentration gradients, and single drug (paclitaxel, gemcitabine, 7rh, fluorouracil, PP2) concentration gradients have been established in the five collagen regions, respectively, so that each localized microchamber in the regions has a unique drug microenvironment. In this way, we evaluated the composite and single chemotherapy efficacy on the same chip by statistically analyzing their effects on the numbers and migration of the cell. The quantitative results in CCECs reveal that the inhibition effects on the numbers and migration of MDA-MB-231-RFP cell under the composite drug gradients are more optimal than those of the single drugs. Besides, the cancer cell inhibition effect between the groups composed of two drugs has also been compared, that is the paclitaxel+gemcitabine, paclitaxel+fluorouracil, and paclitaxel+PP2 have better cell numbers and migration inhibition effects than paclitaxel+7rh. The results indicate that the bio-mimetic and high-throughput combined chemotherapy evaluation platform can serve as a more efficient and accurate tool for preclinical drug development and screening.

Macromolecular crowding and supersaturation protect hemodialysis patients from the onset of dialysis-related amyloidosis.

Dialysis-related amyloidosis (DRA), a serious complication among long-term hemodialysis patients, is caused by amyloid fibrils of ß2-microglobulin (ß2m). Although high serum ß2m levels and a long dialysis vintage are the primary and secondary risk factors for the onset of DRA, respectively, patients with these do not always develop DRA, indicating that there are additional risk factors. To clarify these unknown factors, we investigate the effects of human sera on ß2m amyloid fibril formation, revealing that sera markedly inhibit amyloid fibril formation. Results from over 100 sera indicate that, although the inhibitory effects of sera deteriorate in long-term dialysis patients, they are ameliorated by maintenance dialysis treatments in the short term. Serum albumin prevents amyloid fibril formation based on macromolecular crowding effects, and decreased serum albumin concentration in dialysis patients is a tertiary risk factor for the onset of DRA. We construct a theoretical model assuming cumulative effects of the three risk factors, suggesting the importance of monitoring temporary and accumulated risks to prevent the development of amyloidosis, which occurs based on supersaturation-limited amyloid fibril formation in a crowded milieu.

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology.

In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward the research on the innovation of platform economic business model driven by BP neural network and artificial intelligence technology. BP neural network is used to study and evaluate the competitiveness of B2B e-commerce companies. According to the B2B e-commerce company competitiveness theory and BP neural network algorithm, combined with BP neural network and B2B e-commerce company competitiveness evaluation index system, a BP neural network model is designed to analyze the competitiveness of B2B e-commerce enterprises. Determine the expected value of network samples, select G1 method to determine the subjective weight, and select entropy weight method to determine the objective weight. With the help of the function in the MATLAB neural network toolbox, the neural network is trained. The results show that when the training times reach 3297 times, the sample mean square error is 9.9869e - 06, and the training network reaches convergence. The samples of three enterprises test the trained neural network and input the data of three test samples into the trained BP neural network, and the output results are 0.1531, 0.1371, and 0.1557, respectively. The network model constructed in this paper is effectively close to the training samples. The established BP neural network has good performance and can be used to evaluate the competitiveness of B2B e-commerce companies. Accelerate technological change and realize innovation. Technological capability is the inexhaustible driving force for the development of enterprises. Only with the innovation of keeping pace with the times can application-oriented e-commerce enterprises meet the needs of customers and the market, form the difference between goods or services, and then enable enterprises to win more customers and market share.

Biological gel-based microchamber array for tumor cell proliferation and migration studies in well-controlled biochemical gradients.

Breast cancer metastasis is a complex process controlled by multiple factors, including various cell-cell interactions, cell-environment coupling, and oxygen, nutrient and drug gradients that are intimately related to the heterogeneous breast tissue structure. In this study, we constructed a high-throughput in vitro biochip system containing an array of 642 microchambers arranged in a checkerboard configuration, with each chamber embedded in a composite extracellular matrix (ECM) composed of engineered collagen and Matrigel to mimic local heterogeneous environment in vivo. In addition, a controllable complex tetragonal chemical concentration profile can be achieved by imposing chemical compounds at the four boundaries of the chip, leading to distinct local nutrient and/or drug gradients in the individual microchambers. Here, the microchamber array with composite ECM (MACECM) device aims to simulate multiple tumor cell niches composed of both breast epithelial cells (MCF-10A-GFP) and metastatic breast cancer cells (MDA-MB-231-RFP), which enables systematic studies of cell responses to a variety of biochemical conditions. The results obtained from the MACECM studies indicate that discoidin domain receptor 1 (DDR1) inhibitor 7rh and matrix metalloproteinase inhibitor batimastat, in association with epidermal growth factor (EGF) had no significant effects on the growth of MCF-10A-GFP cells, but had significant effects on DDR1 expression and the related migratory behavior of MDA-MB-231-RFP cells. The MACECM design not only enables the construction of a more realistic in vitro model for investigating cancer cell migration mechanisms but also has considerable potential for further development as a platform for next-generation high-throughput and therapeutic screening (e.g., anti-cancer drug evaluation) and personalized medicine.

Overexpression of HcSCL13, a Halostachys caspica GRAS transcription factor, enhances plant growth and salt stress tolerance in transgenic Arabidopsis.

Salt is a major abiotic stress that negatively impacts plant growth and development. Research on the mechanisms of plant salt tolerance and the breeding of salt-tolerant plants is becoming an important research field. Transcription factors are master regulators that control the expression of many target genes, helping to regulate the response of plants to adverse conditions. GRAS are plant-specific transcription factors that play various roles in plant development and stress responses. However, the function of a GRAS gene identified in Halostachys caspica, a salt-tolerant plant with important ecological value, has not been determined. In this study, we characterized a novel gene (HcSCL13) encoding a GRAS transcription factor from H. caspica. Quantitative real-time (qRT)-PCR results indicated that HcSCL13 expression was induced by salt, drought and application of stress-related phytohormone abscisic acid (ABA). The HcSCL13 protein was localized in the nucleus with transactivation activity at the N terminus. Heterologous overexpression of HcSCL13 enhanced plant growth and salt tolerance in transgenic Arabidopsis. With HcSCL13 overexpression, plants had enhanced growth, as well as greater chlorophyll content, fresh weight and root elongation compared to the wild type. Transcriptomic analysis revealed that HcSCL13 overexpression affected the response to light/abiotic stimulus/hormone/organic substance, plant hormone signal-related and plant growth and development genes under normal and saline stress conditions. Taken together, these results indicate that HcSCL13 genes can modulate salt stress tolerance in Arabidopsis through the regulation of plant growth and the activation of gene expression.

Incident Angle Identification Based on First-Echo Energy Attenuation in Ultrasonic Thickness Measurement.

Ultrasonic pulse technology has been widely employed for thickness measurement in the industrial field. Generally, the ultrasonic transducer is required to be perpendicular to the test sample surface when using the longitudinal wave. However, the transducer posture deviation (i.e., incident angle) of ultrasonic beam relative to the local surface normal is always inevitable. In addition, the received ultrasonic pressure will be weakened significantly because of the variation of echo propagation path and the attenuation of echo amplitude. As a result, the incident angle induced thickness error is generated. Therefore, it is necessary to investigate and quantify the negative effect of incident angle on ultrasonic thickness measurement. This paper focuses on the incident angle identification based on the first-echo energy attenuation. First, the influence mechanism of incident angle on pulse-echo pressure is analyzed theoretically by adopting an equivalent transducer model. Furthermore, a novel approach for the incident identification based on the first-echo energy attenuation is developed. A compensation model is established to correct the incident angle induced thickness error. To verify the feasibility of the proposed method, a series of incident angle calibrations and experiments were designed using the developed ultrasonic pulse measurement system. It was indicated that the proposed angle identification and error compensation approach had the capacity to improve the measurement accuracy of ultrasonic thickness.

Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm.

The efficient data access of streaming vehicle data is the foundation of analyzing, using and mining vehicle data in smart cities, which is an approach to understand traffic environments. However, the number of vehicles in urban cities has grown rapidly, reaching hundreds of thousands in number. Accessing the mass streaming data of vehicles is hard and takes a long time due to limited computation capability and backward modes. We propose an efficient streaming spatio-temporal data access based on Apache Storm (ESDAS) to achieve real-time streaming data access and data cleaning. As a popular streaming data processing tool, Apache Storm can be applied to streaming mass data access and real time data cleaning. By designing the Spout/bolt workflow of topology in ESDAS and by developing the speeding bolt and other bolts, Apache Storm can achieve the prospective aim. In our experiments, Taiyuan BeiDou bus location data is selected as the mass spatio-temporal data source. In the experiments, the data access results with different bolts are shown in map form, and the filtered buses' aggregation forms are different. In terms of performance evaluation, the consumption time in ESDAS for ten thousand records per second for a speeding bolt is approximately 300 milliseconds, and that for MongoDB is approximately 1300 milliseconds. The efficiency of ESDAS is approximately three times higher than that of MongoDB.

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City.

The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems.