Metformin alleviates spinal cord injury by inhibiting nerve cell ferroptosis through upregulation of heme oxygenase-1 expression

Previous studies have reported upregulation of heme oxygenase-1 in different central nervous system injury models.Heme oxygenase-1 plays a critical anti-inflammatory role and is essential for regulating cellular redox homeostasis.Metformin is a classic drug used to treat type 2 diabetes that can inhibit ferroptosis.Previous studies have shown that,when used to treat cardiovascular and digestive system diseases,metformin can also upregulate heme oxygenase-1 expression.Therefore,we hypothesized that heme oxygenase-1 plays a significant role in mediating the beneficial effects of metformin on neuronal ferroptosis after spinal cord injury.To test this,we first performed a bioinformatics analysis based on the GEO database and found that heme oxygenase-1 was upregulated in the lesion of rats with spinal cord injury.Next,we confirmed this finding in a rat model of T9 spinal cord compression injury that exhibited spinal cord nerve cell ferroptosis.Continuous intraperitoneal injection of metformin for 14 days was found to both upregulate heme oxygenase-1 expression and reduce neuronal ferroptosis in rats with spinal cord injury.Subsequently,we used a lentivirus vector to knock down heme oxygenase-1 expression in the spinal cord,and found that this significantly reduced the effect of metformin on ferroptosis after spinal cord injury.Taken together,these findings suggest that metformin inhibits neuronal ferroptosis after spinal cord injury,and that this effect is partially dependent on upregulation of heme oxygenase-1.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

A CRISPR/Cas12a-based platform for rapid on-site bovine viral diarrhea virus diagnostics

Bovine viral diarrhea virus(BVDV)is a positive-sense single-stranded RNA virus,belonging to the genus Pestivirus in the Flaviviridae family(Riitho et al.2020).Currently,BVDV is divided into 3 main genotypes,BVDV-1,BVDV-2,and BVDV-3,based on the genetic differences in the 5′untranslated region(5′UTR)(Muasya et al.2022).

Integrated strategies for chemotherapy cycles in nasopharyngeal carcinoma patients: Real-world data from two epidemic centers guiding decision-making

objective:Two cycles of induction chemotherapy(IC)followed by 2 cycles of platinum-based concurrent chemoradiotherapy(CCRT)(2IC+2CCRT)for locoregionally advanced nasopharyngeal carcinoma(LA-NPC)is widely adopted but not evidence-confirmed.This study aimed to determine the clinical value of 2IC+2CCRT regarding efficacy,toxicity and cost-effectiveness.Methods:This real-world study from two epidemic centers used propensity score matching(PSM)and inverse probability of treatment weighting(IPTW)analyses.The enrolled patients were divided into three groups based on treatment modality:Group A(2IC+2CCRT),Group B(3IC+2CCRT or 2IC+3CCRT)and Group C(3IC+3CCRT).Long-term survival,acute toxicities and cost-effectiveness were compared among the groups.We developed a prognostic model dividing the population into high-and low-risk cohorts,and survivals including overall survival(OS),progression-free survival(PFS),distant metastasis-free survival(DMFS)and locoregional relapse-free survival(LRRFS)were compared among the three groups according to certain risk stratifications.Results:Of 4,042 patients,1,175 were enrolled,with 660,419,and 96 included in Groups A,B and C,respectively.Five-year survivals were similar among the three groups after PSM and confirmed by IPTW.Grade 3-4 neutropenia and leukocytopenia were significantly higher in Groups C and B than in Group A(52.1%vs.41.5%vs.25.2%;41.7%vs.32.7%vs.25.0%)as were grade 3-4 nausea/vomiting and oral mucositis(29.2%vs.15.0%vs.6.1%;32.3%vs.25.3%vs.18.0%).Cost-effective analysis suggested that 2IC+2CCRT was the least expensive,while the health benefits were similar to those of the other groups.Further exploration showed that 2IC+2CCRT tended to be associated with a shorter PFS in high-risk patients,while 3IC+3CCRT potentially contributed to poor PFS in low-risk individuals,mainly reflected by LRRFS.Conclusions:In LA-NPC patients,2IC+2CCRT was the optimal choice regarding efficacy,toxicity and costeffectiveness;however,2IC+2CCRT and 3IC+3CCRT probably shortened LRRFS in high-and low-risk populations,respectively.

Lithiophilicity: The key to efficient lithium metal anodes for lithium batteries

Lithium metal anode of lithium batteries,including lithium-ion batteries,has been considered the anode for next-generation batteries with desired high energy densities due to its high theoretical specific capacity(3860 mA h g^(-1))and low standards electrode potential(-3.04 V vs.SHE).However,the highly reactive nature of metallic lithium and its direct contact with the electrolyte could lead to severe chemical reactions,leading to the continuous consumption of the electrolyte and a reduction in the cycle life and Coulombic efficiency.In addition,the solid electrolyte interface formed during battery cycling is mainly inorganic,which is too fragile to withstand the extreme volume change during the plating and stripping of lithium.The uneven flux of lithium ions could lead to excessive lithium deposition at local points,resulting in needle-like lithium dendrites,which could pierce the separator and cause short circuits,battery failure,and safety issues.In the last five years,tremendous efforts have been dedicated to addressing these issues,and the most successful improvements have been related to lithiophilicity optimizations.Thus,this paper comprehensively reviewed the lithiophilicity regulation in lithium metal anode modifications and highlighted the vital effect of lithiophilicity.The remaining challenges faced by the lithiophilicity optimization for lithium metal anodes are discussed with the proposed research directions for overcoming the technical challenges in this subject.

Challenges in diagnosis of pancreatic cancer

Pancreatic cancer is a growing source of cancer related death, yet has poor survival rates which have not improved in the last few decades. Its high mortality rate is attributed to pancreatic cancer biology, difficulty in early diagnosis and the lack of standardised international guidelines in assessing suspicious pancreatic masses. This review aims to provide an update in the current state of play in pancreatic cancer diagnosis and to evaluate the benefits and limitations of available diagnostic technology. The main modalities discussed are imaging with computed tomography, magnetic resonance imaging, endoscopic ultrasound and positron emission tomography and tissue acquisition with fine needle aspiration. We also review the improvements in the techniques used for tissue acquisition and the opportunity for personalised cancer medicine. Screening of high risk individuals, promising biomarkers and common mimickers of pancreatic cancer are also explored, as well as suggestions for future research directions to allow for earlier detection of pancreatic cancer. Timely and accurate diagnosis of pancreatic cancer can lead to improvements in the current poor outcome of this disease.

Machine learning for pore-water pressure time-series prediction:Application of recurrent neural networks

Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.

Exploration of the potential mechanism of Danggui Shaoyao powder in the treatment of endometriosis based on bioinformatics

Background:Endometriosis is a hormone-and organ-dependent disease with unclear pathogenesis.Danggui Shaoyao powder(DSP)has been used for the treatment of endometriosis for many years and has been proven to be effective,but its underlying molecular mechanisms remain unclear.In this study,we explored the molecular targets and pathways of DSP in the treatment of endometriosis mainly from the perspective of network pharmacology,and provided some novel ideas for managing this disease.Methods:In the present study,we focused on the underlying mechanisms between DPS and endometriosis,via oral bioavailability screening,drug similarity assessment,target identification,network analysis,drug relocation,and molecular docking using network pharmacology.Results:By bioinformatics,54 active components of DSP were predicted from the 534 components provided by the TCMSP database.We then found 85 validated targets and 6059 predicted targets for herbal components,and 2241 targets for endometriosis.After mapping,there were 255 targets in common.After that,network analysis was performed to screen key networks and core targets.Ninetynine indirect targets were collected from 14 direct ones.GO and KEGG analyses identified 63 enriched functions and 10 pathways(P<.01).According to the disease target,dipyridamole could undergo drug repositioning for endometriosis(score?1).The mechanism of action of dipyridamole on endometriosis was compared with that of DSP.Finally,eight targets,namely,AR,ESR1,HMGCR,NOS2,NR3C1,PPARG,PTGES,and PTGS2,were validated by molecular docking,suggesting that the probable molecular mechanisms by which DSP treats endometriosis are through seven validated pathways:hsa05200,hsa04915,hsa01100,hsa00900,hsa04920,hsa01130,and hsa00590.Conclusion:In this study,we uncovered the mechanism by which DSP can treat endometriosis using a drugecomponentetarget interaction network.This approach can provide some novel ideas for the development of Chinese medicine and the clinical management of endometriosis.

A Microfluidic System with Active Mixing for Improved Real-Time Isothermal Amplification

To improve the performance of real-time recombinase polymerase amplification(RPA),a microfluidic system with active mixing is developed to optimize the reaction dynamics.Instead of adopting a single typical reaction chamber,a specific reactor including a relatively large chamber in center with two adjacent zig-zag channels at two sides is integrated into the microfluidic chip.Active mixing is achieved by driving the viscous reagent between the chamber and the channel back and forth periodically with an outside compact peristaltic pump.To avoid reagent evapora-tion,one end of the reactor is sealed with paraffin oil.A hand-held companion device is developed to facilitate real-time RPA amplification within 20 min.The whole area of the reactor is heated with a resistance heater to provide uniform reaction temperature.To achieve real-time monitoring,a compact fluorescence detection module is integrated into the hand-held device.A smartphone with custom application software is adopted to control the hand-held device and display the real-time fluorescence curves.The performances of two cases with and without active on-chip mixing are compared between each other by detecting African swine fever viruses.It has been demonstrated that,with active on-chip mixing,the amplification efficiency and detection sensitivity can be signifi-cantly improved.

Quantum and quasiclassical dynamics of C(^(3)P)+H_(2)(^(1Σ_(g)^(+)))→H(^(2)S)+CH(^(2)Ⅱ)reaction:Coriolis coupling effects and stereodynamics

The dynamics of C+H_(2)→H+CH reaction is theoretically studied using the quasiclassical trajectory and quantum mechanical wave packet methods.The analysis of reaction probabilities,integral cross sections,and rate coefficients reveal the essential Coriolis coupling effects in the quantum mechanical wave packet calculations.The calculated polarizationdependent differential cross section,P(θ_(r))and P(Φ_(r))show that the j'of product rotational angular momentum is not only aligned along the y axis and the direction of the vector x+z,but also strongly oriented along the positive y axis.

Development Opportunities and Challenges of Film and Television Industry in the Post-Epidemic Era

The sudden outbreak of COVID-19 in 2020 has not only caused a serious impact on the development of China's economy,but also on China's cultural industry,especially the film and television industry,which is now entering the post-epidemic era.This paper explains the impact of the epidemic on domestic and foreign film and television industry and the three major impacts on the film industry,and then interprets the response measures of the film and television industry in the post-epidemic era.

Eubacterium coprostanoligenes alleviates chemotherapy-induced intestinal mucositis by enhancing intestinal mucus barrier

Chemotherapy-induced mucositis represents a severe adverse outcome of cancer treatment,significantly curtailing the efficacy of these treatments and,in some cases,resulting in fatal conse-quences.Despite identifying intestinal epithelial cell damage as a key factor in chemotherapy-induced mucositis,the paucity of effective treatments for such damage is evident.In our study,we discovered that Eubacterium coprostanoligenes promotes mucin secretion by goblet cells,thereby fortifying the integrity of the intestinal mucus barrier.This enhanced barrier function serves to resist microbial invasion and sub-sequently reduces the inflammatory response.Importantly,this effect remains unobtrusive to the anti-tumor efficacy of chemotherapy drugs.Mechanistically,E.copr up-regulates the expression of AUF1,leading to the stabilization of Muc2 mRNA and an increase in mucin synthesis in goblet cells.An espe-cially significant finding is that E.copr activates the AhR pathway,thereby promoting the expression of AUF1.In summary,our results strongly indicate that E.copr enhances the intestinal mucus barrier,effec-tively alleviating chemotherapy-induced intestinal mucositis by activating the AhR/AUFl pathway,consequently enhancing Muc2 mRNA stability.

Echinatin inhibits tumor growth and synergizes with chemotherapeutic agents against human bladder cancer cells by activating p38 and suppressing Wnt/β-catenin pathways

Bladder cancer (BC) is one of the most common malignant tumors in the urinary system.Due to the poor prognosis and high mortality rate of the disease,it is urgent to develop new drugs with high efficacy and low toxicity to treat BC.Echinatin (Ecn) is a bioactive natural flavonoid oflicorice that has attracted special attention for its promising anti-tumor potential.Herein,we explored the inhibitory effects of Echinatin on BC cells and probed the possible molecular mechanism.We found that Ecnin vitro inhibited the proliferation,migration,and invasion,arrested the cell cycle at the G2/M phase,and promoted apoptosis in BC cells.Besides,Ecn had no notable cytotoxicity towards human normal cells.We subsequently confirmed that Ecn restrained xenograft tumor growth and metastasis of BC cells in vivo .Mechanistically,Ecn activated the p38 signaling pathway but inactivated the Wnt/β-catenin signaling pathway,while over-expression of β-catenin and the p38 inhibitor both attenuated the inhibitory effects of Ecn on BC cells.Remarkably,Ecn combined with cisplatin (DDP) or gemcitabine (Gem) had synergistic inhibitory effects on BC cells.In summary,our results validate that Ecn inhibits the tumor growth of human BC cells via p38 and Wnt/β-catenin signaling pathways.More meaningfully,our results suggest a potential strategy to enhance DDP- or Gem-induced inhibitory effects on BC cells by combining with Ecn.

Improving pixel-based regional landslide susceptibility mapping

Regional landslide susceptibility mapping(LSM)is essential for risk mitigation.While deep learning algorithms are increasingly used in LSM,their extensive parameters and scarce labels(limited landslide records)pose training challenges.In contrast,classical statistical algorithms,with typically fewer parameters,are less likely to overfit,easier to train,and offer greater interpretability.Additionally,integrating physics-based and data-driven approaches can potentially improve LSM.This paper makes several contributions to enhance the practicality,interpretability,and cross-regional generalization ability of regional LSM models:(1)Two new hybrid models,composed of data-driven and physics-based modules,are proposed and compared.Hybrid ModelⅠcombines the infinite slope stability analysis(ISSA)with logistic regression,a classical statistical algorithm.Hybrid ModelⅡintegrates ISSA with a convolutional neural network,a representative of deep learning techniques.The physics-based module constructs a new explanatory factor with higher nonlinearity and reduces prediction uncertainty caused by incomplete landslide inventory by pre-selecting non-landslide samples.The data-driven module captures the rela-tion between explanatory factors and landslide inventory.(2)A step-wise deletion process is proposed to assess the importance of explanatory factors and identify the minimum necessary factors required to maintain satisfactory model performance.(3)Single-pixel and local-area samples are compared to understand the effect of pixel spatial neighborhood.(4)The impact of nonlinearity in data-driven algorithms on hybrid model performance is explored.Typical landslide-prone regions in the Three Gorges Reservoir,China,are used as the study area.The results show that,in the testing region,by using local-area samples to account for pixel spatial neighborhoods,Hybrid ModelⅠachieves roughly a 4.2%increase in the AUC.Furthermore,models with 30 m resolution land-cover data surpass those using 1000 m resolution data,showing a 5.5%improvement in AUC.The optimal set of explanatory factors includes elevation,land-cover type,and safety factor.These findings reveal the key elements to enhance regional LSM,offering valuable insights for LSM practices.

Source apportionment and specific-source-site risk of quinolone antibiotics for effluent-receiving urban rivers and groundwater in a city, China

There is a large surface-groundwater exchange downstream ofwastewater treatment plants(WWTPs),and antibiotics upstream may influence sites downstream of rivers.Thus,samples from 9 effluent-receiving urban rivers(ERURs)and 12 groundwater sites were collected in Shijiazhuang City in December 2020 and April 2021.For ERURs,8 out of 13 target quinolone antibiotics(QNs)were detected,and the total concentration of QNs in December and April were 100.6-4,398 ng/L and 8.02–2,476 ng/L,respectively.For groundwater,all target QNs were detected,and the total QNs concentration was 1.09–23.03 ng/L for December and 4.54–170.3 ng/L for April.The distribution of QNs was dissimilar between ERURs and groundwater.Most QN concentrations were weakly correlated with land use types in the system.The results of a positive matrix factorization model(PMF)indicated four potential sources of QNs in both ERURs and groundwater,and WWTP effluents were the main source of QNs.From December to April,the contribution of WWTP effluents and agricultural emissions increased,while livestock activities decreased.Singular value decomposition(SVD)results showed that the spatial variation of most QNs was mainly contributed by sites downstream(7.09%-88.86%)of ERURs.Then,a new method that combined the results of SVD and PMF was developed for a specific-source-site risk quotient(SRQ),and the SRQ for QNs was at high level,especially for the sites downstream of WWTPs.Regarding temporal variation,the SRQ for WWTP effluents,aquaculture,and agricultural emissions increased.Therefore,in order to control the antibiotic pollution,more attention should be paid to WWTP effluents,aquaculture,and agricultural emission sources for the benefit of sites downstream of WWTPs.

Ground Passive Microwave Remote Sensing of Atmospheric Profiles Using WRF Simulations and Machine Learning Techniques

Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.

A Hybrid Channel Stock Model for Stock Price Forecasting with Multifaceted Feature Fusion

Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we propose a Hybrid Channel Stock model that incorporates multiple features of basic stock data,K-line charts and technical indicator factors for predicting the closing price of a stock on day n+1.The model combines multiple aspects of data and uses a multi-channel structure including improved CNN-TW,bidirectional LSTM and Transformer network.First,we construct the multi-channel branches of the multi-faceted feature fusion input network model;second,in this paper,we will use the concatenate method to stitch the output of each branch as the input of the rest of the network;the last layer in the network is the fully connected layer,which combines the linear activation function regression to output the predicted prices.Finally,we conducted extensive experiments on the Dow 30,SSH 50 and CSI100 indices.The experimental results show that the Hybrid Channel Stock method has the best performance with the smallest MSE,RMSE,MAE and MAPE compared with existing models.in addition,the experiments on different trading days validate the stability and effectiveness of the model,providing an important reference for investors to make stock investment decisions.

Corrosion Behavior of Graphene Nanosheets Reinforced Magnesium Matrix Composites in Simulated Body Fluids

Magnesium(Mg)alloy is considered as a promising biodegradable implant material but restricted to rapid degradation.Here,the new strategies based on thixomolding process had been explored to utilize the outstanding anti-permeability of graphene nanosheets(GNPs)while inhibit its galvanic corrosion with the matrix,so as to improve the corrosion resistance of composites.The agglomerate of GNPs with 0.9 wt%content is the main reason for the deterioration of corrosion performance due to the formation of micro-galvanic corrosion.The grain refinement of composites with 0.6 wt%content had positive effects on the better corrosion resistance.After process adjusting,the unique distributions of GNPs along grain boundaries play a vital role in improving the corrosion resistance.It can be ascribed to the following mechanisms:(I)The barriers can be established between the Mg matrix and corrosive medium,hence blocking the charge transfer at the interface;(II)The GNPs can effectively promote apatite deposition on the Mg matrix,leading to form dense apatite layers and prevent the further invasion of SBF;(III)The GNPs acting as reinforcements exists in the corrosion layer and apatite layer,impede the apatite layer falling off from the Mg matrix.These findings broaden the horizon for biomedical applications in Mg matrix composites to realize desired performances.

Development and performance evaluation of a culture-independent nanopore amplicon-based sequencing method for accurate typing and antimicrobial resistance profiling in Neisseria gonorrhoeae

Dear Editor,Bacterial antimicrobial resistance(AMR)poses a serious threat to global human health(Antimicrobial Resistance Collaborators,2022).Comprehensive profiling of AMR and accurate molecular typing are important for tracking and controlling the emergence and dissemination of antibiotic-resistant bacteria(Yahara et al.,2021).We previously developed a multiplex amplicon sequencing-based method for directly sequencing AMR-related loci in N.gonorrhoeae from clinical samples(Zhang et al.,2021).

水工岩土工程灾害风险防控研究现状与展望

水工岩土工程在其全生命周期范围内面临多种灾害和灾害链的威胁。科学认知、描述、预测、评价和控制灾害,确保工程与人民生命财产安全,是水工岩土工程灾害风险防控学科的主要任务。本文针对该学科的研究现状和发展战略开展了系统性研究。首先,给出了该学科的定义和特点并归纳了其学科树的树干和分支。其次,从学科树的8个树干出发对该学科的研究现状和存在的问题进行了总结和分析。最后,对该学科的研究前沿与未来发展方向提出了4点建议。