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.

Maintaining moderate levels of hypochlorous acid promotes neural stem cell proliferation and differentiation in the recovery phase of stroke

It has been shown clinically that continuous removal of ischemia/reperfusion-induced reactive oxygen species is not conducive to the recovery of late stroke.Indeed,previous studies have shown that excessive increases in hypochlorous acid after stroke can cause severe damage to brain tissue.Our previous studies have found that a small amount of hypochlorous acid still exists in the later stage of stroke,but its specific role and mechanism are currently unclear.To simulate stroke in vivo,a middle cerebral artery occlusion rat model was established,with an oxygen-glucose deprivation/reoxygenation model established in vitro to mimic stroke.We found that in the early stage(within 24 hours)of ischemic stroke,neutrophils produced a large amount of hypochlorous acid,while in the recovery phase(10 days after stroke),microglia were activated and produced a small amount of hypochlorous acid.Further,in acute stroke in rats,hypochlorous acid production was prevented using a hypochlorous acid scavenger,taurine,or myeloperoxidase inhibitor,4-aminobenzoic acid hydrazide.Our results showed that high levels of hypochlorous acid(200μM)induced neuronal apoptosis after oxygen/glucose deprivation/reoxygenation.However,in the recovery phase of the middle cerebral artery occlusion model,a moderate level of hypochlorous acid promoted the proliferation and differentiation of neural stem cells into neurons and astrocytes.This suggests that hypochlorous acid plays different roles at different phases of cerebral ischemia/reperfusion injury.Lower levels of hypochlorous acid(5 and 100μM)promoted nuclear translocation ofβ-catenin.By transfection of single-site mutation plasmids,we found that hypochlorous acid induced chlorination of theβ-catenin tyrosine 30 residue,which promoted nuclear translocation.Altogether,our study indicates that maintaining low levels of hypochlorous acid plays a key role in the recovery of neurological function.

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.

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.

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.

Approximate Controllability of Second-Order Neutral Stochastic Differential Equations with Infinite Delay and Poisson Jumps

The modelling of risky asset by stochastic processes with continuous paths, based on Brow- nian motions, suffers from several defects. First, the path continuity assumption does not seem reason- able in view of the possibility of sudden price variations （jumps） resulting of market crashes. A solution is to use stochastic processes with jumps, that will account for sudden variations of the asset prices. On the other hand, such jump models are generally based on the Poisson random measure. Many popular economic and financial models described by stochastic differential equations with Poisson jumps. This paper deals with the approximate controllability of a class of second-order neutral stochastic differential equations with infinite delay and Poisson jumps. By using the cosine family of operators, stochastic analysis techniques, a new set of sufficient conditions are derived for the approximate controllability of the above control system. An example is provided to illustrate the obtained theory.

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.

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.

ON GLOBAL MEROMORPHIC SOLUTIONS OF SECOND-ORDER LINEAR DIFFERENTIAL EQUATIONS WITH MEROMORPHIC COEFFICIENTS

The main purpose of this article is to study the existence theories of global meromorphic solutions for some second-order linear differential equations with meromorphic coefficients, which perfect the solution theory of such equations.

Differential Quadrature Method for Steady Flow of an Incompressible Second-Order Viscoelastic Fluid and Heat Transfer Model

The two-dimensional steady flow of an incompressible second-order viscoelastic fluid between two parallel plates was studied in terms of vorticity, the stream function and temperature equations. The governing equations were expanded with respect to a snmll parameter to get the zeroth- and first-order approximate equations. By using the differenl2al quadrature method with only a few grid points, the high-accurate numerical results were obtained.

A Compound Algorithm of Denoising Using Second-Order and Fourth-Order Partial Differential Equations

In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.

EXISTENCE OF SOLUTION AND APPROXIMATE CONTROLLABILITY OF A SECOND-ORDER NEUTRAL STOCHASTIC DIFFERENTIAL EQUATION WITH STATE DEPENDENT DELAY

This paper has two sections which deals with a second order stochastic neutral partial differential equation with state dependent delay. In the first section the existence and uniqueness of mild solution is obtained by use of measure of non-compactness. In the second section the conditions for approximate controllability are investigated for the distributed second order neutral stochastic differential system with respect to the approximate controllability of the corresponding linear system in a Hilbert space. Our method is an extension of co-author N. Sukavanam’s novel approach in [22]. Thereby, we remove the need to assume the invertibility of a controllability operator used by authors in [5], which fails to exist in infinite dimensional spaces if the associated semigroup is compact. Our approach also removes the need to check the invertibility of the controllability Gramian operator and associated limit condition used by the authors in [20], which are practically difficult to verify and apply. An example is provided to illustrate the presented theory.

NEW OSCILLATION CRITERIA FOR SECOND-ORDER NONLINEAR MATRIX DIFFERENTIAL EQUATIONS

Some new oscillation criteria are established for the second-order matrix differential system （r（t）Z＇（t））＇ ＋ p（t）Z＇（t） ＋ Q（t）F（Z＇（t））G（Z（t）） = 0, t ≥ to 〉 0, are different from most known ones in the sense that they are based on the information only on a sequence of subintervals of [to,∞）, rather than on the whole half-line. The results weaken the condition of Q（t） and generalize some well-known results of Wong （1999） to nonlinear matrix differential equation.

Oscillation of Second-order Delay Differential Equation

By using some differential inequality, a second-order delay differential equation(r(t)x′(t))′ + p(t)x(q(t)) = 0has been investigated and some necessary condition for this equation has a nonoscillatorysolution and some sufficient condition which ensures that all of the solutions of the aboveequation are oscillatory are obtained.

Lagrange-Noether method for solving second-order differential equations

The purpose of this paper is to provide a new method called the Lagrange-Noether method for solving second-order differential equations. The method is, firstly, to write the second-order differential equations completely or partially in the form of Lagrange equations, and secondly, to obtain the integrals of the equations by using the Noether theory of the Lagrange system. An example is given to illustrate the application of the result.

POSITIVE SOLUTIONS OF BOUNDARY VALUE PROBLEMS FOR SECOND-ORDER SINGULAR NONLINEAR DIFFERENTIAL EQUATIONS

New existence results are presented for the singular second-order nonlinear boundary value problems u ' + g(t)f(u) = 0, 0 < t < 1, au(0) - betau ' (0) = 0, gammau(1) + deltau ' (1) = 0 under the conditions 0 less than or equal to f(0)(+) < M-1, m(1) < f(infinity)(-)less than or equal to infinity or 0 less than or equal to f(infinity)(+)< M-1, m(1) < f (-)(0)less than or equal to infinity where f(0)(+) = lim(u -->0)f(u)/u, f(infinity)(-)= lim(u --> infinity)f(u)/u, f(0)(-)= lim(u -->0)f(u)/u, f(infinity)(+) = lim(u --> infinity)f(u)/u, g may be singular at t = 0 and/or t = 1. The proof uses a fixed point theorem in cone theory.

NONDEGENERACY OF POSITIVE SOLUTIONS TO HOMOGENEOUS SECOND-ORDER DIFFERENTIAL SYSTEMS AND ITS APPLICATIONS

This article considers the Dirichlet problem of homogeneous and inhomogeneous second-order ordinary differential systems. A nondegeneracy result is proven for positive solutions of homogeneous systems. Sufficient and necessary conditions for the existence of multiple positive solutions for inhomogeneous systems are obtained by making use of the nondegeneracy and uniqueness results of homogeneous systems.

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.

ON ALGEBRAIC SOLUTIONS OF THE SECOND-ORDER COMPLEX DIFFERENTIAL EQUATIONS WITH ENTIRE ALGEBRAIC ELEMENT COEFFICIENTS

The main purpose of this paper is to study the problems on the existence of algebraic solutions for some second-order complex differential equations with entire algebraic function element coeifficients. Several theorems on the existence of solutions are obtained, which perfect the solution theory of linear complex differential equations.

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.