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Although some effective therapies have been available for cancer, it still poses a great threat to human health and life due to its drug resistance and low response in patients. Here, we develop a ferroptosis-based therapy by combining iron nanoparticles and cancer-specific gene interference. The expression of two iron metabolic genes ( FPN and LCN2 ) was selectively knocked down in cancer cells by Cas13a or microRNA controlled by a NF-κB-specific promoter. Cells were simultaneously treated by iron nanoparticles. As a result, a significant ferroptosis was induced in a wide variety of cancer cells. However, the same treatment had little effect on normal cells. By transferring genes with adeno-associated virus and iron nanoparticles, the significant tumor growth inhibition and durable cure were obtained in mice with the therapy. In this work, we thus show a cancer therapy based on gene interference-enhanced ferroptosis. Improved therapeutic strategies are needed as drug resistance limits the therapeutic efficacy of several clinically approved cancer therapeutics. Here, the authors report a ferroptosis-based therapy using a combination of iron nanoparticles with gene interference to knockdown iron metabolic genes, FPN and LCN2.

Background A cluster of pneumonia cases were reported by Wuhan Municipal Health Commission, China in December 2019. A novel coronavirus was eventually identified, and became the COVID-19 epidemic that affected public health and life. We investigated the psychological status and behavior changes of the general public in China from January 30 to February 3, 2020. Methods Respondents were recruited via social media (WeChat) and completed an online questionnaire. We used the State-Trait Anxiety Inventory, Self-rating Depression Scale, and Symptom Checklist-90 to evaluate psychological status. We also investigated respondents’ behavior changes. Quantitative data were analyzed by t -tests or analysis of variance, and classified data were analyzed with chi-square tests. Results In total, 608 valid questionnaires were obtained. More respondents had state anxiety than trait anxiety (15.8% vs 4.0%). Depression was found among 27.1% of respondents and 7.7% had psychological abnormalities. About 10.1% of respondents suffered from phobia. Our analysis of the relationship between subgroup characteristics and psychological status showed that age, gender, knowledge about COVID-19, degree of worry about epidemiological infection, and confidence about overcoming the outbreak significantly influenced psychological status. Around 93.3% of respondents avoided going to public places and almost all respondents reduced Spring Festival-related activities. At least 70.9% of respondents chose to take three or more preventive measures to avoid infection. The three most commonly used prevention measures were making fewer trips outside and avoiding contact (98.0%), wearing a mask (83.7%), and hand hygiene (82.4%). Conclusions We need to pay more attention to public psychological stress, especially among young people, as they are likely to experience anxiety, depression, and psychological abnormalities. Different psychological interventions could be formulated according to the psychological characteristics of different gender and age groups. The majority of respondents followed specific behaviors required by the authorities, but it will take time to observe the effects of these behaviors on the epidemic.

Single-atom Fe-N-C catalysts has attracted widespread attentions in the oxygen reduction reaction (ORR). However, the origin of ORR activity on Fe-N-C catalysts is still unclear, which hinder the further improvement of Fe-N-C catalysts. Herein, we provide a model to understand the ORR activity of Fe-N 4 site from the spatial structure and energy level of the frontier orbitals by density functional theory calculations. Taking the regulation of divacancy defects on Fe-N 4 site ORR activity as examples, we demonstrate that the hybridization between Fe 3 dz 2 , 3 dyz (3 dxz ) and O 2 π* orbitals is the origin of Fe-N 4 ORR activity. We found that the Fe–O bond length, the d-band center gap of spin states, the magnetic moment of Fe site and *O 2 as descriptors can accurately predict the ORR activity of Fe-N 4 site. Furthermore, these descriptors and ORR activity of Fe-N 4 site are mainly distributed in two regions with obvious difference, which greatly relate to the height of Fe 3 d projected orbital in the Z direction. This work provides a new insight into the ORR activity of single-atom M-N-C catalysts. It is of high importance to understand the origin of single-atom Fe-N 4 activity in oxygen reduction reaction. Here, the authors provide a model to understand the catalytic activity of Fe-N 4 site from the spatial structure and energy level of the frontier orbitals by density functional theory calculations.

[...]taking into account the research on AKI to chronic kidney disease (CKD) transition [4], we recommend extending the follow-up period for the 21 patients to at least three months to evaluate whether these differential proteins and alarmins serve as biomarkers for CKD. [...]while circulating alarmins such as HMGB-1 are considered promising biomarkers for AKI [5], no differential expression of it was observed in the proteomics analysis, and their performance in blood samples was suboptimal. [...]the use of human kidney biopsies to identify differentially expressed AKI-related proteins in this study is clinically valuable and provides a theoretical foundation for proposing targets for mechanistic research.

Flexible pressure sensors are indispensable components in various applications such as intelligent robots and wearable devices, whereas developing flexible pressure sensors with both high sensitivity and wide linear range remains a great challenge. Here, we present an elegant strategy to address this challenge by taking advantage of a pyramidal carbon foam array as the sensing layer and an elastomer spacer as the stiffness regulator, realizing an unprecedentedly high sensitivity of 24.6 kPa −1 and an ultra-wide linear range of 1.4 MPa together. Such a wide range of linearity is attributed to the synergy between the nonlinear piezoresistivity of the sensing layer and the nonlinear elasticity of the stiffness regulator. The great application potential of our sensor in robotic manipulation, healthcare monitoring, and human-machine interface is demonstrated. Our design strategy can be extended to the other types of flexible sensors calling for both high sensitivity and wide-range linearity, facilitating the development of high-performance flexible pressure sensors for intelligent robotics and wearable devices. The development of flexible pressure sensors with a wide linear range and high sensitivity is challenging. Chen et al. demonstrate a sensor for robotics and wearable devices, using nonlinear elasticity of an elastic spacer as a stiffness regulator and a microstructured sensing layer with non-linear piezoresistivity.

On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems as empirical examples. Mechanisms of synergistic adjusting out-of-step biogas flow rates on both the plant-side and user-side are defined to obtain consumption-to-production ratios of close to 1, such that biogas demand of rural inhabitants can be met. Furthermore, carbon mitigation and its viability under universal prevailing climates are illustrated. Coupled with manure management optimization, Chinese national deployment of the proposed system would contribute a 3.77% reduction towards meeting its global 1.5 °C target. Additionally, fulfilling others’ energy demands has considerable decarbonization potential. Shen and colleagues demonstrate an improved design for community biogas production and distribution system to overcome common obstacles and achieve full co-benefits in developing economies.

In a forest of a hundred thousand trees, no two leaves are alike. Similarly, no two cells in a genetically identical group are the same. This heterogeneity at the single-cell level has been recognized to be vital for the correct interpretation of diagnostic and therapeutic results of diseases, but has been masked for a long time by studying average responses from a population. To comprehensively understand cell heterogeneity, diverse manipulation and comprehensive analysis of cells at the single-cell level are demanded. However, using traditional biological tools, such as petri-dishes and well-plates, is technically challengeable for manipulating and analyzing single-cells with small size and low concentration of target biomolecules. With the development of microfluidics, which is a technology of manipulating and controlling fluids in the range of micro- to pico-liters in networks of channels with dimensions from tens to hundreds of microns, single-cell study has been blooming for almost two decades. Comparing to conventional petri-dish or well-plate experiments, microfluidic single-cell analysis offers advantages of higher throughput, smaller sample volume, automatic sample processing, and lower contamination risk, etc., which made microfluidics an ideal technology for conducting statically meaningful single-cell research. In this review, we will summarize the advances of microfluidics for single-cell manipulation and analysis from the aspects of methods and applications. First, various methods, such as hydrodynamic and electrical approaches, for microfluidic single-cell manipulation will be summarized. Second, single-cell analysis ranging from cellular to genetic level by using microfluidic technology is summarized. Last, we will also discuss the advantages and disadvantages of various microfluidic methods for single-cell manipulation, and then outlook the trend of microfluidic single-cell analysis.

Among kinds of flexible tactile sensors, piezoelectric tactile sensor has the advantage of fast response for dynamic force detection. However, it suffers from low sensitivity at high-frequency dynamic stimuli. Here, inspired by finger structure—rigid skeleton embedded in muscle, we report a piezoelectric tactile sensor using a rigid-soft hybrid force-transmission-layer in combination with a soft bottom substrate, which not only greatly enhances the force transmission, but also triggers a significantly magnified effect in d 31 working mode of the piezoelectric sensory layer, instead of conventional d 33 mode. Experiments show that this sensor exhibits a super-high sensitivity of 346.5 pC N −1 (@ 30 Hz), wide bandwidth of 5–600 Hz and a linear force detection range of 0.009–4.3 N, which is ~17 times the theoretical sensitivity of d 33 mode. Furthermore, the sensor is able to detect multiple force directions with high reliability, and shows great potential in robotic dynamic tactile sensing. Designing efficient tactile sensors under high-frequency dynamic stimuli remains a challenge. Here, the authors demonstrate piezoelectric tactile sensor with sensitivity of 346.5 pCN−1, wide bandwidth of 5–600 Hz and a linear force detection range of 0.009–4.3 N using a rigid-soft hybrid force-transmission-layer in combination with a soft bottom substrate.

Big data is a hot issue in both theoretical and practical circles. Although many scholars have analyzed the risk of internal control of venture capital information system from different angles, there is still a lack of research on the risk of internal control of venture capital system under big data environment. Aiming at the concept and characteristics of large data, this paper proposes the research of internal control system of venture capital information system based on large data processing technology. The risk prediction model based on improved quantum support vector machine is used to verify the accuracy of the model. This paper divides the risk critical control process one by one for the hardware, software, personnel, information and operation rules of the venture capital object; probes into the main risks of different control objects in the process of information system construction under the big data environment; and puts forward the corresponding risk management methods of system internal control. Simulation experiments verify the reliability of the model and algorithm.