Effects of mesenchymal stem cell on dopaminergic neurons,motor and memory functions in animal models of Parkinson's disease:a systematic review and meta-analysis

Parkinson’s disease is chara cterized by the loss of dopaminergic neurons in the substantia nigra pars com pacta,and although restoring striatal dopamine levels may improve symptoms,no treatment can cure or reve rse the disease itself.Stem cell therapy has a regenerative effect and is being actively studied as a candidate for the treatment of Parkinson’s disease.Mesenchymal stem cells are considered a promising option due to fewer ethical concerns,a lower risk of immune rejection,and a lower risk of teratogenicity.We performed a meta-analysis to evaluate the therapeutic effects of mesenchymal stem cells and their derivatives on motor function,memory,and preservation of dopamine rgic neurons in a Parkinson’s disease animal model.We searched bibliographic databases(PubMed/MEDLINE,Embase,CENTRAL,Scopus,and Web of Science)to identify articles and included only pee r-reviewed in vivo interve ntional animal studies published in any language through J une 28,2023.The study utilized the random-effect model to estimate the 95%confidence intervals(CI)of the standard mean differences(SMD)between the treatment and control groups.We use the systematic review center for laboratory animal expe rimentation’s risk of bias tool and the collaborative approach to meta-analysis and review of animal studies checklist for study quality assessment.A total of 33studies with data from 840 Parkinson’s disease model animals were included in the meta-analysis.Treatment with mesenchymal stem cells significantly improved motor function as assessed by the amphetamine-induced rotational test.Among the stem cell types,the bone marrow MSCs with neurotrophic factor group showed la rgest effect size(SMD[95%CI]=-6.21[-9.50 to-2.93],P=0.0001,I^(2)=0.0%).The stem cell treatment group had significantly more tyrosine hydroxylase positive dopamine rgic neurons in the striatum([95%CI]=1.04[0.59 to 1.49],P=0.0001,I^(2)=65.1%)and substantia nigra(SMD[95%CI]=1.38[0.89 to 1.87],P=0.0001,I^(2)=75.3%),indicating a protective effect on dopaminergic neurons.Subgroup analysis of the amphetamine-induced rotation test showed a significant reduction only in the intracranial-striatum route(SMD[95%CI]=-2.59[-3.25 to-1.94],P=0.0001,I^(2)=74.4%).The memory test showed significant improvement only in the intravenous route(SMD[95%CI]=4.80[1.84 to 7.76],P=0.027,I^(2)=79.6%).Mesenchymal stem cells have been shown to positively impact motor function and memory function and protect dopaminergic neurons in preclinical models of Parkinson’s disease.Further research is required to determine the optimal stem cell types,modifications,transplanted cell numbe rs,and delivery methods for these protocols.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Role of cancer stem cell ecosystem on breast cancer metastasis and related mouse models

Breast cancer metastasis is responsible for most breast cancer-related deaths and is influenced by many factors within the tumor ecosystem,including tumor cells and microenvironment.Breast cancer stem cells(BCSCs)constitute a small population of cancer cells with unique characteristics,including their capacity for self-renewal and differentiation.Studies have shown that BCSCs not only drive tumorigenesis but also play a crucial role in promoting metastasis in breast cancer.The tumor microenvironment(TME),composed of stromal cells,immune cells,blood vessel cells,fibroblasts,and microbes in proximity to cancer cells,is increasingly recognized for its crosstalk with BCSCs and role in BCSC survival,growth,and dissemination,thereby influencing metastatic ability.Hence,a thorough understanding of BCSCs and the TME is critical for unraveling the mechanisms underlying breast cancer metastasis.In this review,we summarize current knowledge on the roles of BCSCs and the TME in breast cancer metastasis,as well as the underlying regulatory mechanisms.Furthermore,we provide an overview of relevant mouse models used to study breast cancer metastasis,as well as treatment strategies and clinical trials addressing BCSC-TME interactions during metastasis.Overall,this study provides valuable insights for the development of effective therapeutic strategies to reduce breast cancer metastasis.

Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma

Background:Cytotoxic T lymphocytes(CD8+T)cells function critically in mediating anti-tumor immune response in cancer patients.Characterizing the specific functions of CD8+T cells in lung adenocarcinoma(LUAD)could help better understand local anti-tumor immune responses and estimate the effect of immunotherapy.Methods:Gens related to CD8+T cells were identified by cluster analysis based on the single-cell sequencing data of three LUAD tissues and their paired normal tissues.Weighted gene co-expression network analysis(WGCNA),consensus clustering,differential expression analysis,least absolute shrinkage and selection operator(LASSO)and Cox regression analysis were conducted to classify molecular subtypes for LUAD and to develop a risk model using prognostic genes related to CD8+T cells.Expression of the genes in the prognostic model,their effects on tumor cell invasion,and interactions with CD8+T cells were verified by cell experiments.Results:This study defined two LUAD clusters(CD8+0 and CD8+1)based on CD8+T cells,with cluster CD8+0 being significantly associated with the prognosis of LUAD.Three heterogeneous subtypes(clusters 1,2,and 3)differing in prognosis,genome mutation events,and immune status were categorized using 42 prognostic genes.A prognostic model created based on 11 significant genes(including CD200R1,CLEC17A,ZC3H12D,GNG7,SNX30,CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2,and KRT81)was able to independently estimate the death risk for patients in different LUAD cohorts.Moreover,the model also showed general applicability in external validation cohorts.Low-risk patients could benefit more from taking immunotherapy and were significantly related to the resistance to anticancer drugs.The results from cell experiments demonstrated that the expression of CD200R1,CLEC17A,ZC3H12D,GNG7,and SNX30 was significantly downregulated,while that of CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2 and KRT81 was upregulated in LUAD cells.Inhibition of CD200R1 greatly increased the invasiveness of the LUAD cells,but inhibiting CDCP1 expression weakened the invasion ability of LUAD cells.Conclusion:This study defined two prognostic CD8+T cell clusters and classified three heterogeneous molecular subtypes for LUAD.A prognostic model predictive of the potential effects of immunotherapy on LUAD patients was developed.

Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

Variability of the Pacific subtropical cells under global warming in CMIP6 models

The Pacific subtropical cells(STCs)are shallow meridional overturning circulations connecting the tropics and subtropics,and are assumed to be an important driver of the tropical Pacific decadal variability.The variability of STCs under global warming is investigated using multimodal outputs from the latest phase of the Coupled Model Inter-comparison Project(CMIP6)and ocean reanalysis products.Firstly,the volume transport diagnostic analysis is employed to evaluate how coupled models and ocean reanalysis products reproduce interior STC transport.The variation of heat transport by the interior STC under the high-emissions warming scenarios is also analyzed.The results show that the multimodal-mean linear trends of the interior STC transport along 9°S and 9°N are-0.02 Sv/a and 0.04 Sv/a under global warming,respectively,which is mainly due to the combined effect of the strengthened upper oceanic stratification and the weakening of wind field.There is a compensation relationship between the interior STC and the western boundary transport in the future climate,and the compensation relationship of 9°S is more significant than that of 9°N.In addition,compared with ocean reanalysis products,the coupled models tend to underestimate the variability of the interior STC transport convergence,and thus may lose some sea surface temperature(SST)driving force,which may be the reason for the low STC-SST correlation simulated by the model.The future scenario simulation shows that the heat transport of interior STC is weakened under global warming,with a general agreement across models.

Elucidating the molecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic model

BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health implications.Cell demise mechanisms play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence.AIM To investigate the impact of ATP-induced cell death(AICD)on breast cancer,enhancing our understanding of its mechanism.METHODS The foundational genes orchestrating AICD mechanisms were extracted from the literature,underpinning the establishment of a prognostic model.Simultaneously,a microRNA(miRNA)prognostic model was constructed that mirrored the gene-based prognostic model.Distinctions between high-and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized,with the aim of delineating common influence mechanisms,substantiated through enrichment analysis and immune infiltration assessment.RESULTS The mRNA prognostic model in this study encompassed four specific mRNAs:P2X purinoceptor 4,pannexin 1,caspase 7,and cyclin 2.The miRNA prognostic model integrated four pivotal miRNAs:hsa-miR-615-3p,hsa-miR-519b-3p,hsa-miR-342-3p,and hsa-miR-324-3p.B cells,CD4+T cells,CD8+T cells,endothelial cells,and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes.Furthermore,Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways,while miRNA risk scores significantly enriched 29 signaling pathways,with 16 pathways being jointly enriched.CONCLUSION Of paramount significance,distinct mRNA and miRNA signature models were devised tailored to AICD,both potentially autonomous prognostic factors.This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools,offering an unparalleled window for innovative interventions.Essentially,this paper reveals the hitherto enigmatic link between AICD and breast cancer,potentially leading to revolutionary progress in personalized oncology.

Acute liver failure:A systematic review and network meta-analysis of optimal type of stem cells in animal models

BACKGROUND The therapeutic effects of various stem cells in acute liver failure(ALF)have been demonstrated in preclinical studies.However,the specific type of stem cells with the highest therapeutic potential has not been determined.AIM To validate the efficacy of stem cells in ALF model and to identify the most promising stem cells.METHODS A search was conducted on the PubMed,Web of Science,Embase,Scopus,and Cochrane databases from inception to May 3,2022,and updated on November 16,2022 to identify relevant studies.Two independent reviewers performed the literature search,identification,screening,quality assessment,and data extraction.RESULTS A total of 89 animal studies were included in the analysis.The results of traditional meta-analysis showed that stem cell therapy could significantly reduce the serum levels of alanine aminotransferase[weighted mean difference(WMD)=-181.05(-191.71,-170.39)],aspartate aminotransferase[WMD=-309.04(-328.45,-289.63)],tumor necrosis factor-alpha[WMD=-8.75(-9.93,-7.56)],and interleukin-6[WMD=-10.43(-12.11,-8.76)]in animal models of ALF.Further subgroup analysis and network meta-analysis showed that although mesenchymal stem cells are the current research hotspot,the effect of liver stem cells(LSCs)on improving liver function is significantly better than that of the other five types of stem cells.In addition,the ranking results showed that the possibility of LSCs improving liver function ranked first.This fully proves the great therapeutic potential of LSCs,which needs to be paid more attention in the future.CONCLUSION LSCs may have a higher therapeutic potential.Further high-quality animal experiments are needed to explore the most effective stem cells for ALF.

Efficiency-loss analysis of monolithic perovskite/silicon tandem solar cells by identifying the patterns of a dual two-diode model’s current-voltage curves

In this work,we developed a simple and direct circuit model with a dual two-diode model that can be solved by a SPICE numerical simulation to comprehensively describe the monolithic perovskite/crystalline silicon(PVS/c-Si)tandem solar cells.We are able to reveal the effects of different efficiency-loss mechanisms based on the illuminated current density-voltage(J-V),semi-log dark J-V,and local ideality factor(m-V)curves.The effects of the individual efficiency-loss mechanism on the tandem cell’s efficiency are discussed,including the exp(V/VT)and exp(V/2VT)recombination,the whole cell’s and subcell’s shunts,and the Ohmic-contact or Schottky-contact of the intermediate junction.We can also fit a practical J-V curve and find a specific group of parameters by the trial-and-error method.Although the fitted parameters are not a unique solution,they are valuable clues for identifying the efficiency loss with the aid of the cell’s structure and experimental processes.This method can also serve as an open platform for analyzing other tandem solar cells by substituting the corresponding circuit models.In summary,we developed a simple and effective methodology to diagnose the efficiency-loss source of a monolithic PVS/c-Si tandem cell,which is helpful to researchers who wish to adopt the proper approaches to improve their solar cells.

Cell reprogramming therapy for Parkinson’s disease

Parkinson’s disease is typically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta.Many studies have been performed based on the supplementation of lost dopaminergic neurons to treat Parkinson’s disease.The initial strategy for cell replacement therapy used human fetal ventral midbrain and human embryonic stem cells to treat Parkinson’s disease,which could substantially alleviate the symptoms of Parkinson’s disease in clinical practice.However,ethical issues and tumor formation were limitations of its clinical application.Induced pluripotent stem cells can be acquired without sacrificing human embryos,which eliminates the huge ethical barriers of human stem cell therapy.Another widely considered neuronal regeneration strategy is to directly reprogram fibroblasts and astrocytes into neurons,without the need for intermediate proliferation states,thus avoiding issues of immune rejection and tumor formation.Both induced pluripotent stem cells and direct reprogramming of lineage cells have shown promising results in the treatment of Parkinson’s disease.However,there are also ethical concerns and the risk of tumor formation that need to be addressed.This review highlights the current application status of cell reprogramming in the treatment of Parkinson’s disease,focusing on the use of induced pluripotent stem cells in cell replacement therapy,including preclinical animal models and progress in clinical research.The review also discusses the advancements in direct reprogramming of lineage cells in the treatment of Parkinson’s disease,as well as the controversy surrounding in vivo reprogramming.These findings suggest that cell reprogramming may hold great promise as a potential strategy for treating Parkinson’s disease.

Combination of a reaction cell and an ultra-high vacuum system for the in situ preparation and characterization of a model catalyst

An in-depth understanding of the structure-activity relationship between the surface structure,chemical composition,adsorption and desorption of molecules,and their reaction activity and selectivity is necessary for the rational design of high-performance catalysts.Herein,we present a method for studying catalytic mechanisms using a combination of in situ reaction cells and surface science techniques.The proposed system consists of four parts:preparation chamber,temperatureprogrammed desorption(TPD)chamber,quick load-lock chamber,and in situ reaction cell.The preparation chamber was equipped with setups based on the surface science techniques used for standard sample preparation and characterization,including an Ar+sputter gun,Auger electron spectrometer,and a low-energy electron diffractometer.After a well-defined model catalyst was prepared,the sample was transferred to a TPD chamber to investigate the adsorption and desorption of the probe molecule,or to the reaction cell,to measure the catalytic activity.A thermal desorption experiment for methanol on a clean Cu(111)surface was conducted to demonstrate the functionality of the preparation and TPD chambers.Moreover,the repeatability of the in situ reaction cell experiment was verified by CO_(2) hydrogenation on the Ni(110)surface.At a reaction pressure of 800 Torr at 673 K,turnover frequencies for the methanation reaction and reverse water-gas shift reaction were 0.15 and 7.55 Ni atom^(-1) s^(-1),respectively.

Designing a risk prognosis model based on natural killer cell-linked genes to accurately evaluate the prognosis of gastric cancer

Background:This study was aimed at identifying natural killer(NK)cell-related genes to design a risk prognosis model for the accurate evaluation of gastric cancer(GC)prognosis.Methods:We obtained NK cell-related genes from various databases,followed by Cox regression analysis and molecular typing to identify prognostic genes.Various immune algorithms and enrichment analyses were used to investigate the mutations,immune status,and pathway variations among different genotypes.The key prognostic genes were assessed using the least absolute shrinkage and selection operator(Lasso)regression analysis and univariate Cox regression analysis.Thereafter,the risk score(RS)prognosis model was constructed based on the selected important prognostic genes.A Receiver Operating Characteristics(ROC)curve was plotted for analyzing the robustness of the model.Subsequently,the decision and calibration curves were used for assessing the reliability and prediction accuracy of the proposed model.The‘pRRophetic’R software package was utilized for predicting the half-maximal inhibitory concentration(IC50)of immunotherapy and chemotherapy drugs.Results:We screened 21 prognostic genes and three molecular subtypes and found that the C1 subtype had the worst prognosis.Further,the pathways promoting tumor proliferation,such as epithelial-mesenchymal transition were significantly up-regulated.The results also showed that the macrophages in the M2 stage were significantly infiltrated in the C1 subtype,and there was significant overexpression in the C1 subtype,accompanied by a severe inflammatory reaction.The C1 was highly sensitive to drugs like 5-fluorouracil and paclitaxel.The ROC,calibration curve,and decision curve showed that the risk model was robust and strongly reliable.Conclusion:Overall,our proposed NK cell-related RS model can be used as a more accurate prediction index for GC patients,providing a valuable contribution to personalized medicine.

A SARS-CoV-2 neutralizing antibody discovery by single cell sequencing and molecular modeling

Although vaccines have been developed,mutations of SARS-CoV-2,especially the dominant B.1.617.2(delta)and B.1.529(omicron)strains with more than 30 mutations on their spike protein,have caused a significant decline in prophylaxis,calling for the need for drug improvement.Antibodies are drugs preferentially used in infectious diseases and are easy to get from immunized organisms.The current study combined molecular modeling and single memory B cell sequencing to assess candidate sequences before experiments,providing a strategy for the fabrication of SARS-CoV-2 neutralizing antibodies.A total of 128 sequences were obtained after sequencing 196 memory B cells,and 42 sequences were left after merging extremely similar ones and discarding incomplete ones,followed by homology modeling of the antibody variable region.Thirteen candidate sequences were expressed,of which three were tested positive for receptor binding domain recognition but only one was confirmed as having broad neutralization against several SARS-CoV-2 variants.The current study successfully obtained a SARS-CoV-2 antibody with broad neutralizing abilities and provided a strategy for antibody development in emerging infectious diseases using single memory B cell BCR sequencing and computer assistance in antibody fabrication.

Modeling cell contractility responses to acoustic tweezing cytometry

Acoustic tweezing cytometry(ATC)is a recently developed method for cell mechanics regulation.Tar-geted microbubbles,which are attached to integrins and subsequently the actin cytoskeleton,anchor,amplify and transmit the mechanical energy in an acoustic field inside the cells,eliciting prominent cy-toskeleton contractile force increases in various cell types.We propose that a mechanochemical con-version mechanism is critical for the high efficiency of ATC to activate cell contractility responses.Our models predict key experimental observations.Moreover,we study the influences of ATC parameters(ul-trasound center frequency,pulse repetition frequency,duty cycle,and acoustic pressure),cell areas,the number of ATC stimuli,and extracellular matrix rigidity on cell contractility responses to ATC.The simu-lation results suggest that it is large molecules,rather than small ions,that facilitate global responses to the local ATC stimulation,and the incorporation of visible stress fiber bundles improves the accuracy of modeling.

Towards system genetics analysis of head and neck squamous cell carcinoma using the mouse model,cellular platform,and clinical human data

Head and neck squamous cell cancer(HNSCC)is a leading global malignancy.Every year,More than 830000 people are diagnosed with HNSCC globally,with more than 430000 fatalities.HNSCC is a deadly diverse malignancy with many tumor locations and biological characteristics.It originates from the squamous epithelium of the oral cavity,oropharynx,nasopharynx,larynx,and hypopharynx.The most frequently impacted regions are the tongue and larynx.Previous investigations have demonstrated the critical role of host genetic susceptibility in the progression of HNSCC.Despite the advances in our knowledge,the improved survival rate of HNSCC patients over the last 40 years has been limited.Failure to identify the molecular origins of development of HNSCC and the genetic basis of the disease and its biological heterogeneity impedes the development of new therapeutic methods.These results indicate a need to identify more genetic factors underlying this complex disease,which can be better used in early detection and prevention strategies.The lack of reliable animal models to investigate the underlying molecular processes is one of the most significant barriers to understanding HNSCC tumors.In this report,we explore and discuss potential research prospects utilizing the Collaborative Cross mouse model and crossing it to mice carrying single or double knockout genes(e.g.Smad 4 and P53 genes)to identify genetic factors affecting the development of this complex disease using genome-wide association studies,epigenetics,micro RNA,long noncoding RNA,lnc RNA,histone modifications,methylation,phosphorylation,and proteomics.

Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic environment.The specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network coverage.Nowa-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network time.The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration failure.As the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital role.To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC.The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale,two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.

Geometric regulation of collective cell tangential ordering migration

Collective cell migration is a coordinated movement of multi-cell systems essential for various processes throughout life.The collective motions often occur under spatial restrictions,hallmarked by the collective rotation of epithelial cells confined in circular substrates.Here,we aim to explore how geometric shapes of confinement regulate this collective cell movement.We develop quantitative methods for cell velocity orientation analysis,and find that boundary cells exhibit stronger tangential ordering migration than inner cells in circular pattern.Furthermore,decreased tangential ordering movement capability of collective cells in triangular and square patterns are observed,due to the disturbance of cell motion at unsmooth corners of these patterns.On the other hand,the collective cell rotation is slightly affected by a convex defect of the circular pattern,while almost hindered with a concave defect,also resulting from different smoothness features of their boundaries.Numerical simulations employing cell Potts model well reproduce and extend experimental observations.Together,our results highlight the importance of boundary smoothness in the regulation of collective cell tangential ordering migration.

Trimethylamine N-oxide aggravates vascular permeability and endothelial cell dysfunction under diabetic condition:in vitro and in vivo study

AIM:To provide the direct evidence for the crucial role of trimethylamine N-oxide(TMAO)in vascular permeability and endothelial cell dysfunction under diabetic condition.METHODS:The role of TMAO on the in vitro biological effect of human retinal microvascular endothelial cells(HRMEC)under high glucose conditions was tested by a cell counting kit,wound healing,a transwell and a tube formation assay.The inflammation-related gene expression affected by TMAO was tested by real-time polymerase chain reaction(RT-PCR).The expression of the cell junction was measured by Western blotting(WB)and immunofluorescence staining.In addition,two groups of rat models,diabetic and non-diabetic,were fed with normal or 0.1%TMAO for 16wk,and their plasma levels of TMAO,vascular endothelial growth factor(VEGF),interleukin(IL)-6 and tumor necrosis factor(TNF)-αwere tested.The vascular permeability of rat retinas was measured using FITC-Dextran,and the expression of zonula occludens(ZO)-1 and claudin-5 in rat retinas was detected by WB or immunofluorescence staining.RESULTS:TMAO administration significantly increased the cell proliferation,migration,and tube formation of primary HRMEC either in normal or high-glucose conditions.RT-PCR showed elevated inflammation-related gene expression of HRMEC under TMAO stimulation,while WB or immunofluorescence staining indicated decreased cell junction ZO-1 and occludin expression after high-glucose and TMAO treatment.Diabetic rats showed higher plasma levels of TMAO as well as retinal vascular leakage,which were even higher in TMAO-feeding diabetic rats.Furthermore,TMAO administration increased the rat plasma levels of VEGF,IL-6 and TNF-αwhile decreasing the retinal expression levels of ZO-1 and claudin-5.CONCLUSION:TMAO enhances the proliferation,migration,and tube formation of HRMEC,as well as destroys their vascular integrity and tight connection.It also regulates the expression of VEGF,IL-6,and TNF-α.

Human pluripotent stem cell-derived kidney organoids:Current progress and challenges

Human pluripotent stem cell(hPSC)-derived kidney organoids share similarities with the fetal kidney.However,the current hPSC-derived kidney organoids have some limitations,including the inability to perform nephrogenesis and lack of a corticomedullary definition,uniform vascular system,and coordinated exit path-way for urinary filtrate.Therefore,further studies are required to produce hPSC-derived kidney organoids that accurately mimic human kidneys to facilitate research on kidney development,regeneration,disease modeling,and drug screening.In this review,we discussed recent advances in the generation of hPSC-derived kidney organoids,how these organoids contribute to the understanding of human kidney development and research in disease modeling.Additionally,the limitations,future research focus,and applications of hPSC-derived kidney organoids were highlighted.