Research on quantitative detection technology of raccoon-derived ingredient adulteration in sausage products.

This project presents a quantitative detection method to identify raccoon-derived ingredient adulteration in sausage products. The specific copy gene of the raccoon was selected as the target gene. According to the specificity of its primer and probe, the quantitative detection method of raccoon microdrops by droplet digital PCR was established. In addition, the accuracy of the proposed method was verified by artificially mixed samples, and the applicability of this method was tested based on the commercially available products. The experimental results indicate that the raccoon mass (M) and raccoon-extracted DNA concentration have a good linear relationship when the sample content is 5-100 mg, and there is also a significant linear relationship between DNA content and DNA copy number (C) with R 2 = .9982. Therefore, using DNA concentration as the median signal, the conversion equation between raw raccoon mass (M) and DNA copy number (C) could be obtained as follows: M = (C + 177.403)/16.954. The detection of artificially mixed samples and commercial samples shows that the method is accurate and suitable for quantitative adulteration detection of various sausage products in the market.

Association and mediation between educational attainment and respiratory diseases: a Mendelian randomization study.

BACKGROUND: Respiratory diseases are a major health burden, and educational inequalities may influence disease prevalence. We aim to evaluate the causal link between educational attainment and respiratory disease, and to determine the mediating influence of several known modifiable risk factors. METHODS: We conducted a two-step, two-sample Mendelian randomization (MR) analysis using summary statistics from genome-wide association studies (GWAS) and single nucleotide polymorphisms (SNPs) as instrumental variables for educational attainment and respiratory diseases. Additionally, we performed a multivariable MR analysis to estimate the direct causal effect of each exposure variable included in the analysis on the outcome, conditional on the other exposure variables included in the model. The mediating roles of body mass index (BMI), physical activity, and smoking were also assessed. FINDINGS: MR analyses provide evidence of genetically predicted educational attainment on the risk of FEV1 (ß = 0.10, 95% CI 0.06, 0.14), FVC (ß = 0.12, 95% CI 0.07, 0.16), FEV1/FVC (ß = - 0.005, 95% CI - 0.05, 0.04), lung cancer (OR = 0.54, 95% CI 0.45, 0.65) and asthma (OR = 0.86, 95% CI 0.78, 0.94). Multivariable MR dicated the effect of educational attainment on FEV1 (ß = 0.10, 95% CI 0.04, 0.16), FVC (ß = 0.07, 95% CI 0.01, 0.12), FEV1/FVC (ß = 0.07, 95% CI 0.01, 0.01), lung cancer (OR = 0.55, 95% CI 0.42, 0.71) and asthma (OR = 0.88, 95% CI 0.78, 0.99) persisted after adjusting BMI and cigarettes per day. Of the 23 potential risk factors, BMI, smoking may partially mediate the relationship between education and lung disease. CONCLUSION: High levels of educational attainment have a potential causal protective effect on respiratory diseases. Reducing smoking and adiposity may be a target for the prevention of respiratory diseases attributable to low educational attainment.

A Variable Stiffness Soft Gripper Based on Rotational Layer Jamming.

This article presents the design and fabrication of a variable stiffness soft gripper based on layer jamming. Traditional layer jamming units have some limitations, such as complicated multistep fabrication, difficulties in system integration, and diminishing in stiffen effect. In this article, a variable stiffness soft gripper is proposed based on the rotational jamming layers to reduce the slippery phenomenon between layers. To fabricate the proposed complex design, a two-step fabrication method is presented. First, multimaterial 3D printing is applied to directly print out the soft finger body with jamming layers. Second, mold casting is used to fabricate the outer vacuum chamber. The proposed gripper contains a main framework and three identical variable stiffness soft fingers. To demonstrate the effectiveness of the design, the soft gripper is mounted on a robotic arm to test its ability of grasping heavy objects while following complex grasping trajectory. The gripper can successfully grasp an object up to 360 g. Grasping robustness of the proposed gripper can be guaranteed when the robotic arm is moving at acceleration up to 7 m/s2. The results prove that the proposed design of the soft gripper can improve the grippers grasping robustness during high-speed movement.

Humic substances as electron acceptor for anaerobic oxidation of methane (AOM) and electron shuttle in Mn (IV)-dependent AOM.

Anaerobic methanotrophic archaea (ANME) belonging to the family Methanoperedenaceae are crucial for the global carbon cycle and different biogeochemical processes, owing to their metabolic versatility to couple anaerobic oxidation of methane (AOM) with different electron acceptors. A universal feature of Methanoperedenaceae is the abundant genes encoded in their genomes associated with extracellular electron transfer (EET) pathways. Candidatus. 'Methanoperedens manganicus', an archaeon belonging to the family Methanoperedenaceae, was recently enriched in a bioreactor performing AOM coupled with Mn (IV) reduction. Using this EET-capable ANME, we tested the hypothesis in this study that ANME can catalyse the humic-dependent AOM for growth. A two-year incubation showed that AOM activity can be sustained by Ca. 'M. manganicus' consortium in a bioreactor fed only with humic acids and methane. An isotopic mass balance batch test confirmed that the observed AOM was coupled to the reduction of humic acids. The increase of relative abundance of Ca. 'M. manganicus', and the total archaea population in the microbial community suggested that Ca. 'M. manganicus' can grow on methane and humic acids. The observation of humic-dependent AOM led to a subsequent hypothesis that humic acids could be used as the electron shuttle to mediate the EET in dissimilatory Mn (IV) reduction by Ca. 'M. manganicus'. We tested this hypothesis by adding humic acids to a Ca. 'M. manganicus' dominated-culture, which showed that the AOM rate was doubled by the addition of humic acids. X-ray photoelectron spectroscopy (XPS) showed that quinone moieties were consumed when humic acids worked as electron acceptors while remaining stable when functioning as a shuttle for electron transfer. The results of our study suggest that humic acids may serve as electron shuttles to allow ANME to access more electron acceptors through long-range EET.

Training multi-source domain adaptation network by mutual information estimation and minimization.

We address the problem of Multi-Source Domain Adaptation (MSDA), which trains a neural network using multiple labeled source datasets and an unlabeled target dataset, and expects the trained network to well classify the unlabeled target data. The main challenge in this problem is that the datasets are generated by relevant but different joint distributions. In this paper, we propose to address this challenge by estimating and minimizing the mutual information in the network latent feature space, which leads to the alignment of the source joint distributions and target joint distribution simultaneously. Here, the estimation of the mutual information is formulated into a convex optimization problem, such that the global optimal solution can be easily found. We conduct experiments on several public datasets, and show that our algorithm statistically outperforms its competitors. Video and code are available at https://github.com/sentaochen/Mutual-Information-Estimation-and-Minimization.

Pyrogenic Carbon Promotes Anaerobic Oxidation of Methane Coupled with Iron Reduction via the Redox-Cycling Mechanism.

Pyrogenic carbon (PC) can mediate electron transfer and thus catalyze biogeochemical processes to impact greenhouse gas (GHG) emissions. Here, we demonstrate that PC can contribute to mitigating GHG emissions by promoting the Fe(III)-dependent anaerobic oxidation of methane (AOM). It was found that the amendment PCs in microcosms dominated by Methanoperedenaceae performing Fe(III)-dependent AOM simultaneously promoted the rate of AOM and Fe(III) reduction with a consistent ratio close to the theoretical stoichiometry of 1:8. Further correlation analysis showed that the AOM rate was linearly correlated with the electron exchange capacity, but not the conductivity, of added PC materials, indicating the redox-cycling electron transfer mechanism to promote the Fe(III)-dependent AOM. The mass content of the C═O moiety from differentially treated PCs was well correlated with the AOM rate, suggesting that surface redox-active quinone groups on PCs contribute to facilitating Fe(III)-dependent AOM. Further microbial analyses indicate that PC likely shuttles direct electron transfer from Methanoperedenaceae to Fe(III) reduction. This study provides new insight into the climate-cooling impact of PCs, and our evaluation indicates that the PC-facilitated Fe(III)-dependent AOM could have a significant contribution to suppressing methane emissions from the world's reservoirs.

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks.

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

Association of ambient PM10 and PM2.5 with coronary stenosis measured using selective coronary angiography.

BACKGROUND: Long-term ambient particulate matter (PM) exposure exerts detrimental effects on cardiovascular health. Evidence on the relation of chronically exposed ambient PM10 and PM2.5 with coronary stenosis remains lacking. Our aim was to investigate the association of PM10 and PM2.5 with coronary stenosis in patients undergoing coronary angiography. METHODS: We performed a retrospective cohort study consisting of 7513 individuals who underwent coronary angiography in Fujian Province, China, from January 2019 to December 2021. We calculated a modified Gensini score (GS) to represent the degree of stenosis in coronary arteries by selective coronary angiography. We fitted linear regressions and logistic models to assess the association of PM10 and PM2.5 with coronary stenosis. We employed restricted cubic splines to describe the exposure-response curves. We performed mediation analyses to assess the potential mediators. RESULTS: Long-term ambient PM10 and PM2.5 (prior three years average) exposure was significantly associated with the GS, with a breakpoint concentration of 47.5 µg/m3 and 25.8 µg/m3 for PM10 and PM2.5, respectively, above which we found a linear positive exposure-response relationship of ambient PM with GS. Each 10 µg /m3 increase in PM10 exposure (ß: 4.81, 95 % CI: 0.44-9.19) and PM2.5 exposure [ß: 10.50, 95 % CI: 3.14-17.86] were positively related to the GS. The adjusted odds ratio (OR) for each 10 µg/m3 increment in PM10 exposure on severe coronary stenosis was 1.33 (95 % CI: 1.04-1.76). Correspondingly, the adjusted OR for PM2.5 was 1.87 (95 % CI: 1.24-2.99). The mediation analysis indicated that the effect of PM10 on coronary stenosis may be partially mediated through total cholesterol, low-density lipoprotein cholesterol, apolipoprotein B, serum creatinine and blood urea nitrogen, and the effect of PM2.5 may be mediated in part by hemoglobin A1c. CONCLUSION: Our study provides the first evidence that chronic ambient PM10 and PM2.5 exposure was associated with coronary stenosis assessed by GS in patients with suspected coronary artery disease and reveals its potential mediators.

Unsupervised Feature Selection via Controllable Adaptive Graph Learning and Discriminative Feature Learning.

Unsupervised feature selection is challenging in machine learning, pattern recognition, and data mining. The crucial difficulty is to learn a moderate subspace that preserves the intrinsic structure and to find uncorrelated or independent features simultaneously. The most common solution is first to project the original data into a lower dimensional space and then force them to preserve the similar intrinsic structure under linear uncorrelation constraint. However, there are three shortcomings. First, the final graph generated by the iterative learning process differs significantly from the initial graph in which the original intrinsic structure is embedded. Second, it requires prior knowledge about a moderate dimension of subspace. Third, it is inefficient when dealing with high-dimensional datasets. The first shortcoming, which is longstanding and undiscovered, makes the previous methods fail to achieve their expected results. The last two ones increase the difficulty of applying in different fields. Therefore, two unsupervised feature selection methods are proposed based on controllable adaptive graph learning and uncorrelated/independent feature learning (CAG-U and CAG-I) to address the abovementioned issues. In the proposed methods, the final graph that preserves intrinsic structure can be adaptively learned while the difference between the two graphs can be well controlled. Besides, relatively uncorrelated/independent features can be selected using a discrete projection matrix. The experimental results on 12 datasets in different fields show the superiority of CAG-U and CAG-I.

Ultrasensitive Linear Capacitive Pressure Sensor with Wrinkled Microstructures for Tactile Perception.

Ultrasensitive flexible pressure sensors with excellent linearity are essential for achieving tactile perception. Although microstructured dielectrics have endowed capacitive sensors with ultrahigh sensitivity, the compromise of sensitivity with increasing pressure is an issue yet to be resolved. Herein, a spontaneously wrinkled MWCNT/PDMS dielectric layer is proposed to realize the excellent sensitivity and linearity of capacitive sensors for tactile perception. The synergistic effect of a high dielectric constant and wrinkled microstructures enables the sensor to exhibit linearity up to 21 kPa with a sensitivity of 1.448 kPa-1 and a detection limit of 0.2 Pa. Owing to these merits, the sensor monitors subtle physiological signals such as various arterial pulses and respiration. This sensor is further integrated into a fully multimaterial 3D-printed soft pneumatic finger to realize material hardness perception. Eight materials with different hardness values are successfully discriminated, and the capacitance of the sensor varies linearly (R2 > 0.975) with increasing hardness. Moreover, the sensitivity to the material hardness can be tuned by controlling the inflation pressure of the soft finger. As a proof of concept, the finger is used to discriminate pork fats with different hardness, paving the way for hardness discrimination in clinical palpation.

Association between solid cooking fuel and cognitive decline: Three nationwide cohort studies in middle-aged and older population.

BACKGROUND: Household solid-fuel burning contributes to indoor air pollution and is linked to poor cognitive function, but how solid cooking fuel use leads to cognitive decline over time is not well elaborated. OBJECTIVE: We examine the associations of solid cooking fuel with cognitive function among three nationally representative cohorts. METHODS: This study uses data from the 2010-2018 China Family Panel Studies (CFPS), the 2011-2018 China Health and Retirement Longitudinal Study (CHARLS) and the 2003-2015 Mexican Health and Aging Study (MHAS) in adults over the age of 50. Time varying Cox model was conducted to measure the association between cooking fuel types and cognitive decline. Mediation analysis was used to estimate the potential mediation effects on the associations of cooking fuel types with cognitive decline risk. RESULTS: Respondents in CFPS, CHARLS, and MHAS relied on solid cooking fuel at baseline approximately 56 %, 51 %, and 12 %, respectively. Using solid fuel was consistently associated with higher risk of cognitive decline in three cohorts (CFPS: HR = 1.300 [95 % CI: 1.201, 1.407], CHARLS: HR = 1.179 [95 % CI: 1.059, 1.312], MHAS: HR = 1.237 [95 % CI: 1.123, 1.362]). Compared to those with persistent solid fuel, persistent clean fuel and change from solid fuel to clean fuel were associated with a lower risk of cognitive decline. Hypertension, diabetes, physical activity, dyslipidemia and high-density lipoprotein cholesterol (HDL-C) may partially mediate the cognitive decline caused by solid fuel use. Of the cognitive decline burden, 18.23 % (95 % CI: 12.21 %, 24.73 %) in CFPS, 8.90 % (95 % CI: 2.93 %, 15.52 %) in CHARLS and 2.92 % (95 % CI: 1.52 %, 4.46 %) in MHAS of cognitive decline cases attributable to solid cooking fuel use. CONCLUSION: The use of solid cooking fuel is associated with a higher risk of cognitive decline. It is essential to promote the expanded use of clean fuel to protect cognitive health.

How Does Organizational Career Management Benefit Employees? The Impact of the "Enabling" and "Energizing" Paths of Organizational Career Management on Employability and Job Burnout.

Organizational career management (OCM) is believed to be a useful practice to stimulate the potential of employees. However, how this can be achieved is still under investigation. This research aims to explore the mechanisms that explain the effects of OCM by clarifying its impact on employees' psychological states and their capability, based on a socially embedded model of thriving. To examine our hypotheses, we conducted a three-wave survey study with 272 full-time employees in China from diverse industries. The study lasted for three months and there was a one-month interval after each wave. We asked the participants to report OCM, career plateau and demographic variables at Time 1, their appraisal of learning and vitality at Time 2, and their self-perceived employability and job burnout at Time 3. We utilized regression analysis to examine our theoretical model and path analysis using the bias-corrected bootstrap method to test the significance of the indirect and moderation effects. The findings showed that OCM positively affected employees' learning and vitality at work, which increased their self-perceived employability and subsequently decreased job burnout. Furthermore, the effects of OCM were found to be weaker for employees with a high degree of career plateau. These findings demonstrate that OCM benefits employees by "enabling" and "energizing" them to better themselves in terms of their employment and they shed light on the boundary condition of the career plateau. Therefore, organizations may provide OCM to facilitate employees' capability and their motivation to engage in self-development, and to further enhance the effects by decreasing their perception of a career plateau.

Multichannel Image Completion With Mixture Noise: Adaptive Sparse Low-Rank Tensor Subspace Meets Nonlocal Self-Similarity.

Multichannel image completion with mixture noise is a common but complex problem in the fields of machine learning, image processing, and computer vision. Most existing algorithms devote to explore global low-rank information and fail to optimize local and joint-mode structures, which may lead to oversmooth restoration results or lower quality restoration details. In this study, we propose a novel model to deal with multichannel image completion with mixture noise based on adaptive sparse low-rank tensor subspace and nonlocal self-similarity (ASLTS-NS). In the proposed model, a nonlocal similar patch matching framework cooperating with Tucker decomposition is used to explore information of global and joint modes and optimize the local structure for improving restoration quality. In order to enhance the robustness of low-rank decomposition to data missing and mixture noise, we present an adaptive sparse low-rank regularization to construct robust tensor subspace for self-weighing importance of different modes and capturing a stable inherent structure. In addition, joint tensor Frobenius and l1 regularizations are exploited to control two different types of noise. Based on alternating directions method of multipliers (ADMM), a convergent learning algorithm is designed to solve this model. Experimental results on three different types of multichannel image sets demonstrate the advantages of ASLTS-NS under five complex scenarios.

Macrophage membrane-camouflaged lipoprotein nanoparticles for effective obesity treatment based on a sustainable self-reinforcement strategy.

Modern lifestyle has led to an increase in the incidence of obesity as a public health concern; however, current anti-obesity medications often show limited efficacy with severe side effects. Therapeutic drugs that are selectively delivered to adipose tissue and accelerate energy consumption are promising strategies to overcome the limitations of existing anti-obesity treatment approaches. Herein, a drug delivery platform based on a macrophage cell membrane (Ma)-camouflaged recombinant high-density lipoprotein (rHDL) that was further decorated with a P3 peptide was fabricated to realize targeted drug delivery to adipose tissue. By co-delivering rosiglitazone (Rosi), a peroxisome proliferator-activated receptor-gamma (PPAR-γ) agonist, and sildenafil (Sild), a phosphodiesterase type 5 (PDE5) inhibitor, a synergistic therapeutic outcome was achieved in the regulation of diet-induced obesity in a mice model. Body weight reduction and the metabolic status of obese mice were significantly improved after 28 days of treatment. More importantly, a sustainable self-reinforcement effect in multidose therapy was found after using this delivery system. The continuous treatment increased prohibitin (PHB) expression and capillary density in adipose tissue, which in turn improved the accumulation of the drugs in subsequent administration. Taken together, this constructed drug delivery system showed high effectiveness with good safety by combining two anti-obesity therapeutic agents, which exhibits promising research potential for adipose-targeted delivery. STATEMENT OF SIGNIFICANCE: Therapeutic strategies that directly target adipose tissue to increase energy consumption and regulate metabolism are promising but challenging. Herein, an adipose tissue-targeted delivery system was developed using a reconstituted high-density lipoprotein (rHDL) coated by a P3 peptide-decorated macrophage membrane. For the first time, we combined rosiglitazone (Rosi) and sildenafil (Sild) in the system and achieved synergy of adipose browning and angiogenesis for anti-obesity treatment. The therapy induced prohibitin expression and angiogenesis, which improved drug accumulation in adipose tissue in subsequent administrations. This resulted in a sustainable self-reinforcement effect with improved capacity for diet-induced obesity regulation. This study highlights the combination of adipose browning and angiogenesis in anti-obesity treatment and provides an innovative concept of enhancing adipose-targeted delivery.

IL-18/IL-18R Signaling Is Dispensable for ILC Development But Constrains the Growth of ILCP/ILCs.

Innate lymphoid cells (ILCs) develop from ILC progenitors in the bone marrow. Various ILC precursors (ILCPs) with different ILC subset lineage potentials have been identified based on the expression of cell surface markers and ILC-associated key transcription factor reporter genes. This study characterized an interleukin (IL)-7Rα+IL-18Rα+ ILC progenitor population in the mouse bone marrow with multi-ILC lineage potential on the clonal level. Single-cell gene expression analysis revealed the heterogeneity of this population and identified several subpopulations with specific ILC subset-biased gene expression profiles. The role of IL-18 signaling in the regulation of IL-18Rα+ ILC progenitors and ILC development was further investigated using Il18- and Il18r1-deficient mice, in vitro differentiation assay, and adoptive transfer model. IL-18/IL-18R-mediated signal was found to not be required for early stages of ILC development. While Il18r1-/- lymphoid progenitors were able to generate all ILC subsets in vitro and in vivo like the wild-type counterpart, increased IL-18 level, as often occurred during infection or under stress, suppressed the growth of ILCP/ILC in an IL-18Ra-dependent manner via inhibiting proliferation and inducing apoptosis.

A Nonlocal Self-Similarity-Based Weighted Tensor Low-Rank Decomposition for Multichannel Image Completion With Mixture Noise.

Multichannel image completion with mixture noise is a challenging problem in the fields of machine learning, computer vision, image processing, and data mining. Traditional image completion models are not appropriate to deal with this problem directly since their reconstruction priors may mismatch corruption priors. To address this issue, we propose a novel nonlocal self-similarity-based weighted tensor low-rank decomposition (NSWTLD) model that can achieve global optimization and local enhancement. In the proposed model, based on the corruption priors and the reconstruction priors, a pixel weighting strategy is given to characterize the joint effects of missing data, the Gaussian noise, and the impulse noise. To discover and utilize the accurate nonlocal self-similarity information to enhance the restoration quality of the details, the traditional nonlocal learning framework is optimized by employing improved index determination of patch group and handling strip noise caused by patch overlapping. In addition, an efficient and convergent algorithm is presented to solve the NSWTLD model. Comprehensive experiments are conducted on four types of multichannel images under various corruption scenarios. The results demonstrate the efficiency and effectiveness of the proposed model.

Vincristine Promotes Transdifferentiation of Fibroblasts Into Myofibroblasts via P38 and ERK Signal Pathways.

Background: Vincristine (VCR) is used in the clinic as an anti-tumor drug. VCR can cause pulmonary fibrosis (PF), leading to respiratory failure. The transformation of fibroblasts into myofibroblasts may play a key role in PF. The present study attempted to reveal the molecular mechanism of VCR-induced PF and the possible involvement of the mitogen-activated protein kinase (MAPK) signaling pathway. Methods: Human embryonic lung fibroblasts (HELFs) were treated with different concentrations of VCR. Inhibitors of extracellular signal-regulated kinase 1/2 (ERK1/2) and p38 MAPK were added to HELFs. Cell proliferation state was assessed using cell counting kit-8 and by directly counting the number of cells. The expressions of vimentin and α-smooth muscle actin (α-SMA) were investigated using western blot and immunofluorescence analyses. Activation of ERK and P38 was estimated by the expression of phosphorylated p38 MAPK (p-p38), p38 MAPK, phosphorylated ERK1/2 (p-ERK1/2) and ERK1/2 using western blot analysis. Enzyme-linked immunosorbent assay was used to estimate the level of collagen I in cell culture supernatants. Results: Results showed that VCR promoted cellular proliferation, secretion of collagen I and the expression of vimentin and α-SMA. High expression of p-p38 and p-ERK1/2 was associated with the activation of the MAPK signaling pathway. MAPK inhibitors SB203580 and PD98059 suppressed the expression of the above proteins. Conclusion: Our study revealed that VCR could promote the differentiation of fibroblasts into myofibroblasts by regulating the MAPK signal pathway, which may be a promising way to treat VCR-induced PF.

Intracranial delivery of synthetic mRNA to suppress glioblastoma.

Owing to messenger RNA's unique biological advantages, it has received increasing attention to be used as a therapeutic, known as mRNA-based gene therapy. It is critical to have an ideal strategy of mRNA gene therapy for glioma, which grows in a special environment. In the present study, we screened out a safe and efficient transfection reagent for intracranial delivery of synthetic mRNA in mouse brain. First, in order to analyze the effect of different transfection reagents on the intracranial delivery of mRNA, the synthetic luciferase mRNA was wrapped with two different transfection reagents and microinjected into the brain at the fixed point. The expression status of delivered mRNA was monitored by a small animal imaging system. The possible reagent-induced biological toxicity was evaluated by behavioral and blood biochemical measurements. Then, to test the therapeutic effect of our intracranial delivery mRNA model on glioma, synthetic modified tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) mRNA was used as an example of therapeutic application. This model demonstrated that synthetic mRNA could be successfully delivered into the brain using commercially available transfection reagents, and TransIT-mRNA showed better results than in vivo-jetPEI kit. This model can be applied in precise targeting and personalized gene therapy of glioma.

Multifunctional Soft Robotic Finger Based on a Nanoscale Flexible Temperature-Pressure Tactile Sensor for Material Recognition.

Robotic hands with tactile perception can perform more advanced and safer operations, such as material recognition. Nanowires with high sensitivity, fast response, and low power consumption are suitable for multifunctional flexible tactile sensors to provide the tactile perception of robotic hands. In this work, we designed a multifunctional soft robotic finger with a built-in nanoscale temperature-pressure tactile sensor for material recognition. The flexible multifunctional tactile sensor integrates a nanowire-based temperature sensor and a conductive sponge pressure sensor to measure the temperature change rate and contact pressure simultaneously. The developed nanoscale temperature and conductive sponge pressure sensor can reach a high sensitivity of 1.196%/°C and 13.29%/kPa, respectively. With this multifunctional tactile sensor, the soft finger can quickly recognize four metals within three contact pressure ranges and 13 materials within a high contact pressure range. By combining tactile information and artificial neural networks, the soft finger can recognize the materials precisely with a high recognition accuracy of 92.7 and 95.9%, respectively. This work proves the application potential of the multifunctional soft robot finger in material recognition.