How mesenchymal stem cells transform into adipocytes:Overview of the current understanding of adipogenic differentiation

Mesenchymal stem cells(MSCs)are stem/progenitor cells capable of self-renewal and differentiation into osteoblasts,chondrocytes and adipocytes.The transformation of multipotent MSCs to adipocytes mainly involves two subsequent steps from MSCs to preadipocytes and further preadipocytes into adipocytes,in which the process MSCs are precisely controlled to commit to the adipogenic lineage and then mature into adipocytes.Previous studies have shown that the master transcription factors C/enhancer-binding protein alpha and peroxisome proliferation activator receptor gamma play vital roles in adipogenesis.However,the mechanism underlying the adipogenic differentiation of MSCs is not fully understood.Here,the current knowledge of adipogenic differentiation in MSCs is reviewed,focusing on signaling pathways,noncoding RNAs and epigenetic effects on DNA methylation and acetylation during MSC differentiation.Finally,the relationship between maladipogenic differentiation and diseases is briefly discussed.We hope that this review can broaden and deepen our understanding of how MSCs turn into adipocytes.

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture(hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector(OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that:(1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km^(2). In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak.(2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced.(3) The results of OPGD detection in 2015 show that the influence of each factor is slope(0.2371) > traffic accessibility(0.2098) > population(0.1403) > regional GDP(0.1325) > elevation(0.0987) > poverty alleviation(0). The results of OPGD detection in 2020 show that the influence of each factor is slope(0.2339) > traffic accessibility(0.2198) > population(0.1432) > regional GDP(0.1219) > poverty alleviation(0.0992) > elevation(0.093). Natural geographical factors(slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors(traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decisionmaking basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies.

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

以色彩为媒介的Design Thinking在艺术设计中的创新实践

色彩作为Design Thinking中的重要媒介,在艺术设计创新实践中具有重要潜力。本文探讨了以色彩为媒介的Design Thinking在艺术设计创新实践中的应用。色彩不仅在Design Thinking中占据重要地位,而且对人类的情绪和行为产生影响,同时在设计中起着至关重要的作用。将Design Thinking引入艺术实践领域,旨在培养艺术创新思维和问题解决能力,强调创新实践和跨学科合作。职业院校在艺术设计创新实践中面临着诸多挑战,但也存在机遇。通过案例分析,本文展示了如何实施基于色彩的Design Thinking艺术设计实践以及这种创新对艺术创新能力的积极影响。

基于“Design Thinking”理念的职业院校创新创业人才培养模式

随着我国经济不断发展和社会进步,创新创业已经成为推动经济增长的关键驱动力,在2015年全国人民代表大会上,李克强总理将“大众创业、万众创新”明确列为我国经济增长的“双引擎”之一,彰显了创新创业对于推动社会发展的重要地位。为响应国家政策,各高校积极开展创新创业教育,将其纳入高等教育综合改革的重要议程,成为培养未来人才的重要途径。本文分析了“Design Thinking”理念下职业院校创新创业人才培养的必要性,围绕新时期专业院校创新创业人才培养的要求,提出了基于“Design Thinking”理念的职业院校创新创业人才培养模式的有效策略,为职业院校人才培养效率的强化提供了参考性意见。

The Effects of Disruptive Thinking on the Food Industry*

This study aimed to understand disruptive thinking and how its ideas can change the food industry. This was achieved by identifying, studying, and understanding the impacts, current trends, and different disruptive ideas and innovations emerging in the food industry. The study was conducted through interpretive research philosophy by carrying out secondary data collection processes, where both qualitative and quantitative information was presented. Deductive approaches were also selected to apply existing theories and models, which were used to construct research hypotheses and present detailed findings. The study finds that, with disruptive thinking, enhancements in the product life cycle, new flavors, and improvements in food packaging have been possible. The supply chain, which is always considered a complex part of the food industry, has been streamlined, offering greater transparency and real-time tracking and improving quality control across distribution systems.

Exploring Critical Thinking Dispositions among Fourth Year BSc Nursing Students of Various Colleges of Nursing, Pakistan

Background: Critical Thinking (CT) dispositions in nursing are prominent predictors of competence in delivering high-quality care, and of professionalism, in newly graduated nurses. CT skills, in isolation of CT dispositions, do not guarantee success in the workplace, because Critical Thinking Dispositions (CTD) are important elements of intellectual reasoning that simulate a person towards using the CT skills. Therefore, nursing educational programs should promote lifelong learning rather than focusing on transferring the content of nursing knowledge only. And for this purpose, quality education is the key. Education should focus on teaching from diverse perspectives, incorporating various teaching learning strategies that are congruent with the modern era. Purpose: The purpose of this study is to explore critical thinking dispositions among final year Baccalaureate Nursing students of various military colleges of nursing, in Pakistan. Methodology: A descriptive qualitative exploratory study design was used to investigate the CTD of BSc final year nursing students. The study population included twelve willing nursing students, from six military colleges across the country. Demographic information and consent was taken from the participants of the study. In-depth interviews, through a semi structured interview guide, and probes were used to obtain data related to personal experiences of CTD amongst the nursing students. Results: Data analysis showed two broad themes: 1) Perceptions of CT, and 2) Experiences of CT dispositions. In theme one, the emerging category was: Clarity of CT;whereas in theme two, the categories that emerged were: a) Truth Seeking, b) Open Mindedness, c) Inquisitiveness, and d) Self Organization. Conclusion: The findings of the study revealed positive dispositions towards truth seeking, open mindedness, and self-organization, whereas disposition towards inquisitiveness was weak. Self-confidence and maturity also emerged as positive factors that the students possessed. This study recommends that faculty and learners should extend their concept of CTD, and emphasizes its application in daily routine. Additionally, faculty should modify their instructional strategies and focus on the cultivation of dispositions of inquisitiveness, curiosity, and allow questioning by students in the class.

Prognostic significance of grade of malignancy based on histopathological differentiation and Ki-67 in pancreatic ductal adenocarcinoma

Objective:Tumor cell malignancy is indicated by histopathological differentiation and cell proliferation.Ki-67,an indicator of cellular proliferation,has been used for tumor grading and classification in breast cancer and neuroendocrine tumors.However,its prognostic significance in pancreatic ductal adenocarcinoma(PDAC)remains uncertain.Methods:Patients who underwent radical pancreatectomy for PDAC were retrospectively enrolled,and relevant prognostic factors were examined.Grade of malignancy(GOM),a novel index based on histopathological differentiation and Ki-67,is proposed,and its clinical significance was evaluated.Results:The optimal threshold for Ki-67 was determined to be 30%.Patients with a Ki-67 expression level>30%rather than≤30%had significantly shorter 5-year overall survival(OS)and recurrence-free survival(RFS).In multivariate analysis,both histopathological differentiation and Ki-67 were identified as independent prognostic factors for OS and RFS.The GOM was used to independently stratify OS and RFS into 3 tiers,regardless of TNM stage and other established prognostic factors.The tumor-nodemetastasis-GOM stage was used to stratify survival into 5 distinct tiers,and surpassed the predictive performance of TNM stage for OS and RFS.Conclusions:Ki-67 is a valuable prognostic indicator for PDAC.Inclusion of the GOM in the TNM staging system may potentially enhance prognostic accuracy for PDAC.

Contrast-enhanced ultrasound for predicting differentiation degree of hepatocellular carcinoma

Objective To explore the value of contrast-enhanced ultrasound(CEUS)for predicting differentiation degree of hepatocellular carcinoma(HCC).Methods Totally 86 HCC patients confirmed by postoperative pathology were retrospectively enrolled and divided into poorly differentiated,moderately differentiated and highly differentiated groups according to postoperative Edmondson-Steiner grading.Preoperative CEUS parameters were compared among groups,and binary logistic regression was used to analyze CEUS-related independent predictors of HCC with different differentiation.The receiver operating characteristic curves of parameters being significant different among groups were drawn,the areas under the curve(AUC)were calculated,and the efficacy for predicting HCC with different differentiation degree was evaluated.Results There were 29 cases in poorly differentiated group,37 in moderately differentiated group and 20 cases in highly differentiated group.The arrival time of contrast agent in poorly differentiated group was earlier than that in moderately and high differentiated groups(both P<0.05),while in moderately differentiated group was not significantly different with that in highly differentiated group(P>0.05).The washout grade were significantly different between each 2 groups(all P<0.05).The arrival time of contrast agent and washout grade were independent predictors of highly or poorly differentiated,moderately or poorly differentiated,moderate-highly or poorly differentiated HCC,and washout grade also was independent predictor of highly or moderately differentiated HCC(all P<0.05).The AUC of the arrival time of contrast agent for predicting highly or moderately differentiated,highly or poorly differentiated,moderately or poorly differentiated,moderate-highly or poorly differentiated HCC was 0.615,0.787,0.690 and 0.724,respectively,while of washout grade was 0.801,0.927,0.795 and 0.841,respectively.Conclusion CEUS could be used to effectively predict differentiation degree of HCC.

An overview of autophagy in the differentiation of dental stem cells

Dental stem cells(DSCs)have attracted significant interest as autologous stem cells since they are easily accessible and give a minimal immune response.These properties and their ability to both maintain self-renewal and undergo multi-lineage differentiation establish them as key players in regenerative medicine.While many regulatory factors determine the differentiation trajectory of DSCs,prior research has predominantly been based on genetic,epigenetic,and molecular aspects.Recent evidence suggests that DSC differentiation can also be influenced by autophagy,a highly conserved cellular process responsible for maintaining cellular and tissue homeostasis under various stress conditions.This comprehensive review endeavors to elucidate the intricate regulatory mechanism and relationship between autophagy and DSC differentiation.To achieve this goal,we dissect the intricacies of autophagy and its mechanisms.Subsequently,we elucidate its pivotal roles in impacting DSC differentiation,including osteo/odontogenic,neurogenic,and angiogenic trajectories.Furthermore,we reveal the regulatory factors that govern autophagy in DSC lineage commitment,including scaffold materials,pharmaceutical cues,and the extrinsic milieu.The implications of this review are far-reaching,underpinning the potential to wield autophagy as a regulatory tool to expedite DSC-directed differentiation and thereby promote the application of DSCs within the realm of regenerative medicine.

The environment,especially the minimum temperature,affects summer maize grain yield by regulating ear differentiation and grain development

Ear differentiation,grain development and their interaction with factors in the growing environment,such as temperature,solar radiation and precipitation,greatly influence grain number and grain weight,and ultimately affect summer maize production.In this study,field experiments involving different sowing dates were conducted over three years to evaluate the effects of temperature factors,average solar radiation and total precipitation on the growth process,ear differentiation,fertilization characteristics,grain filling and yield of summer maize varieties with different growth durations.Four hybrids were evaluated in Huang-Huai-Hai Plain(HHHP),China from 2018 to 2020 with five different sowing dates.The results showed that the grain yield formation of summer maize was strongly impacted by the environment from the silking(R1)to milking(R3)stage.Average minimum temperature(AT_(min))was the key environmental factor that determined yield.Reductions in the length of the growing season(r=–0.556,P<0.01)and the total floret number on ear(R^(2)=0.200,P<0.001)were found when AT_(min) was elevated from the emerging(VE)to R1 stage.Both grain-filling rate(R^(2)=0.520,P<0.001)and the floret abortion rate on ear(R^(2)=0.437,P<0.001)showed quadratic relationships with AT_(min) from the R1 to physiological maturity(R6)stage,while the number of days after the R1 stage(r=–0.756,P<0.01)was negatively correlated with AT_(min).An increase in AT_(min) was beneficial for the promotion of yield when it did not exceeded a certain level(above 23°C during the R1–R3 stage and 20–21°C during the R1-R6 stage).Enhanced solar radiation and precipitation during R1–R6 increased the grain-filling rate(R^(2)=0.562,P<0.001 and R^(2)=0.229,P<0.05,respectively).Compared with short-season hybrids,full-season hybrids showed much greater suitability for a critical environment.The coordinated regulation of AT_(min),ear differentiation and grain development at the pre-and post-silking stages improved maize yield by increasing total floret number and grain-filling rate,and by reducing the floret abortion rate on ear.

Comparative proteomics analysis reveals the domesticated Lepista sordida primordium differentiation regulation mechanism and the subsequent different development patterns in the pileus and stipe

The wild Lepista sordida is a kind of precious and rare edible fungus.An excellent strain of it by artificial domestication was obtained,which was high-yield and high in iron content.In this study,high-throughput comparative proteomics was used to reveal the regulatory mechanism of its primordium differentiation in the early fruiting body formation.The mycelium before the primordium differentiation mainly expressed high levels of mitochondrial functional proteins and carbon dioxide concentration regulatory proteins.In young mushrooms,the highly expressed proteins were mainly involved in cell component generation,cell proliferation,nitrogen compound metabolism,nucleotide metabolism,glutathione metabolism,and purine metabolism.The differential regulation patterns of pileus and stipe growth to maturity were also revealed.The highly expressed proteins related to transcription,RNA splicing,the production of various organelles,DNA conformational change,nucleosome organization,protein processing,maturation and transport,and cell detoxification regulated the pileus development and maturity.The proteins related to carbohydrate and energy metabolism,large amounts of obsolete cytoplasmic parts,nutrient deprivation,and external stimuli regulated the stipe development and maturity.Multiple CAZymes regulated nutrient absorption,morphogenesis,spore production,stress response,and other life activities at different growth and development stages.

O-linkedβ-N-acetylglucosaminylation may be a key regulatory factor in promoting osteogenic differentiation of bone marrow mesenchymal stromal cells

Cumulative evidence suggests that O-linkedβ-N-acetylglucosaminylation(OGlcNAcylation)plays an important regulatory role in pathophysiological processes.Although the regulatory mechanisms of O-GlcNAcylation in tumors have been gradually elucidated,the potential mechanisms of O-GlcNAcylation in bone metabolism,particularly,in the osteogenic differentiation of bone marrow mesenchymal stromal cells(BMSCs)remains unexplored.In this study,the literature related to O-GlcNAcylation and BMSC osteogenic differentiation was reviewed,assuming that it could trigger more scholars to focus on research related to OGlcNAcylation and bone metabolism and provide insights into the development of novel therapeutic targets for bone metabolism disorders such as osteoporosis.

Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.Support Vector Machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

Researching the Impact of the Quality of Village Governance on Farmers’Well-Being:From the Perspectives of Income and Occupational Differentiation

The goal of village governance is to improve the well-being of farmers,so this study aims to measure the impact the quality of village governance on the well-being of farmers.It also examines the heterogeneity of this impact across different farmer groups from the perspectives of income levels and occupational differentiation.To this end,this study developed an indicator system based on survey data collected from 1,442 farmers in the Sichuan,Shaanxi,and Gansu provinces,as well as the Ningxia Hui autonomous region.Multiple linear regression models were then used to analyze this data,and the findings revealed that improvements in the quality of village governance significantly increased the well-being of farmers.Specifically,primary-level empowerment and capacity building were shown to contribute the most to the enhancement of the farmers’well-being,followed by social inclusion,and social cohesion was found to have only a minimal effect.In terms of income levels,improving the quality of village governance benefited middle-income farmers the most,followed by low-income farmers,and it had the least effect on high-income farmers.In terms of occupations,full-time farmers gained the most from improvements in the quality of village governance,followed by off-farm farmers,with part-time farmers benefiting the least.Based on these findings,this study suggests that policymakers should improve the quality of village governance to enhance the well-being of farmers,focusing on the impact that level of income and occupational differentiation have on village governance.

Photobiomodulation:a novel approach to promote trans-differentiation of adipose-derived stem cells into neuronal-like cells

Photobiomodulation,originally used red and near-infrared lasers,can alter cellular metabolism.It has been demonstrated that the visible spectrum at 451-540 nm does not necessarily increase cell proliferation,near-infrared light promotes adipose stem cell proliferation and affects adipose stem cell migration,which is necessary for the cells homing to the site of injury.In this in vitro study,we explored the potential of adipose-derived stem cells to differentiate into neurons for future translational regenerative treatments in neurodegenerative disorders and brain injuries.We investigated the effects of various biological and chemical inducers on trans-differentiation and evaluated the impact of photobiomodulation using 825 nm near-infrared and 525 nm green laser light at 5 J/cm2.As adipose-derived stem cells can be used in autologous grafting and photobiomodulation has been shown to have biostimulatory effects.Our findings reveal that adipose-derived stem cells can indeed trans-differentiate into neuronal cells when exposed to inducers,with pre-induced cells exhibiting higher rates of proliferation and trans-differentiation compared with the control group.Interestingly,green laser light stimulation led to notable morphological changes indicative of enhanced trans-differentiation,while near-infrared photobiomodulation notably increased the expression of neuronal markers.Through biochemical analysis and enzyme-linked immunosorbent assays,we observed marked improvements in viability,proliferation,membrane permeability,and mitochondrial membrane potential,as well as increased protein levels of neuron-specific enolase and ciliary neurotrophic factor.Overall,our results demonstrate the efficacy of photobiomodulation in enhancing the trans-differentiation ability of adipose-derived stem cells,offering promising prospects for their use in regenerative medicine for neurodegenerative disorders and brain injuries.

RPLP0/TBP are the most stable reference genes for human dental pulp stem cells under osteogenic differentiation

BACKGROUND Validation of the reference gene(RG)stability during experimental analyses is essential for correct quantitative real-time polymerase chain reaction(RT-qPCR)data normalisation.Commonly,in an unreliable way,several studies use genes involved in essential cellular functions[glyceraldehyde-3-phosphate dehydro-genase(GAPDH),18S rRNA,andβ-actin]without paying attention to whether they are suitable for such experimental conditions or the reason for choosing such genes.Furthermore,such studies use only one gene when Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines recom-mend two or more genes.It impacts the credibility of these studies and causes dis-tortions in the gene expression findings.For tissue engineering,the accuracy of gene expression drives the best experimental or therapeutical approaches.We cultivated DPSCs under two conditions:Undifferentiated and osteogenic dif-ferentiation,both for 35 d.We evaluated the gene expression of 10 candidates for RGs[ribosomal protein,large,P0(RPLP0),TATA-binding protein(TBP),GAPDH,actin beta(ACTB),tubulin(TUB),aminolevulinic acid synthase 1(ALAS1),tyro-sine 3-monooxygenase/tryptophan 5-monooxygenase activation protein,zeta(YWHAZ),eukaryotic translational elongation factor 1 alpha(EF1a),succinate dehydrogenase complex,subunit A,flavoprotein(SDHA),and beta-2-micro-globulin(B2M)]every 7 d(1,7,14,21,28,and 35 d)by RT-qPCR.The data were analysed by the four main algorithms,ΔCt method,geNorm,NormFinder,and BestKeeper and ranked by the RefFinder method.We subdivided the samples into eight subgroups.RESULTS All of the data sets from clonogenic and osteogenic samples were analysed using the RefFinder algorithm.The final ranking showed RPLP0/TBP as the two most stable RGs and TUB/B2M as the two least stable RGs.Either theΔCt method or NormFinder analysis showed TBP/RPLP0 as the two most stable genes.However,geNorm analysis showed RPLP0/EF1αin the first place.These algorithms’two least stable RGs were B2M/GAPDH.For BestKeeper,ALAS1 was ranked as the most stable RG,and SDHA as the least stable RG.The pair RPLP0/TBP was detected in most subgroups as the most stable RGs,following the RefFinfer ranking.CONCLUSION For the first time,we show that RPLP0/TBP are the most stable RGs,whereas TUB/B2M are unstable RGs for long-term osteogenic differentiation of human DPSCs in traditional monolayers.

Applying Design Thinking in Ecotourism Curriculum Design: The Educational Design Ladder

The design thinking approach transforms student’s learning process to connect with real-world experience, making connections between knowledge and improving critical thinking and problem-solving capability. Previous research has focused on what design thinking is taught (content) and how it is taught (learning modes and assessment). However, there needs to be an integrative approach in the context of ecotourism education. Therefore, applying Wrigley and Straker’s Educational Design Ladder mode, this study develops an Ecotourism Design Thinking Curriculum framework to combine the ecotourism content, teaching/learning mode and assessment.

Exercise promotes osteogenic differentiation by activating the long non-coding RNA H19/microRNA-149 axis

BACKGROUND Regular physical activity during childhood and adolescence is beneficial to bone development,as evidenced by the ability to increase bone density and peak bone mass by promoting bone formation.AIM To investigate the effects of exercise on bone formation in growing mice and to investigate the underlying mechanisms.METHODS 20 growing mice were randomly divided into two groups:Con group(control group,n=10)and Ex group(treadmill exercise group,n=10).Hematoxylin-eosin staining,immunohistochemistry,and micro-CT scanning were used to assess the bone formation-related indexes of the mouse femur.Bioinformatics analysis was used to find potential miRNAs targets of long non-coding RNA H19(lncRNA H19).RT-qPCR and Western Blot were used to confirm potential miRNA target genes of lncRNA H19 and the role of lncRNA H19 in promoting osteogenic differentiation.RESULTS Compared with the Con group,the expression of bone morphogenetic protein 2 was also significantly increased.The micro-CT results showed that 8 wk moderate-intensity treadmill exercise significantly increased bone mineral density,bone volume fraction,and the number of trabeculae,and decreased trabecular segregation in the femur of mice.Inhibition of lncRNA H19 significantly upregulated the expression of miR-149 and suppressed the expression of markers of osteogenic differentiation.In addition,knockdown of lncRNA H19 significantly downregulated the expression of autophagy markers,which is consistent with the results of autophagy-related protein changes detected in mouse femurs by immunofluorescence.CONCLUSION Appropriate treadmill exercise can effectively stimulate bone formation and promote the increase of bone density and bone volume in growing mice,thus enhancing the peak bone mass of mice.The lncRNA H19/miR-149 axis plays an important regulatory role in osteogenic differentiation.