Effects of antioxidant‑rich Lactiplantibacillus plantarum inoculated alfalfa silage on rumen fermentation, antioxidant and immunity status, and mammary gland gene expression in dairy goats

Background Milk synthesis in lactating animals demands high energy metabolism,which results in an increased production of reactive oxygen metabolites(ROM)causing an imbalance between oxidants and antioxidants thereby inducing oxidative stress(OS)on the animals.To mitigate OS and postpartum disorders in dairy goats and gain insight into the impact of dietary choices on redox status during lactation,a feeding trial was conducted using alfalfa silage inoculated with a high-antioxidant strain of Lactiplantibacillus plantarum.Methods Twenty-four Guanzhong dairy goats(38.1±1.20 kg)were randomly assigned to two dietary treatments:one containing silage inoculated with L.plantarum MTD/1(RSMTD-1),and the other containing silage inoculated with high antioxidant activity L.plantarum 24-7(ES24-7).Results ES24-7-inoculated silage exhibited better fermentation quality and antioxidant activity compared to RSMTD-1.The ES24-7 diet elevated the total antioxidant capacity(T-AOC),superoxide dismutase(SOD),glutathione peroxi-dase(GSH-Px),and catalase(CAT)activities in milk,serum,and feces of lactating goats(with the exception of T-AOC in milk).Additionally,the diet containing ES24-7 inoculated silage enhanced casein yield,milk free fatty acid(FFA)content,and vitamin A level in the goats’milk.Furthermore,an increase of immunoglobulin(Ig)A,IgG,IgM,inter-leukin(IL)-4,and IL-10 concentrations were observed,coupled with a reduction in IL-1β,IL-2,IL-6,interferon(IFN)-γ,and tumor necrosis factor(TNF)-αconcentrations in the serum of lactating goats fed ES24-7.Higher concentrations of total volatile fatty acid(VFA),acetate,and propionate were observed in the rumen fluid of dairy goats fed ES24-7 inoculated silage.Moreover,the diet containing ES24-7 inoculated silage significantly upregulated the expression of nuclear factor erythroid 2 like 2(NFE2L2),beta-carotene oxygenase 1(BCO1),SOD1,SOD2,SOD3,GPX2,CAT,glu-tathione-disulfide reductase(GSR),and heme oxygenase 1(HMOX1)genes in the mammary gland,while decreased the levels of NADPH oxidase 4(NOX4),TNF,and interferon gamma(IFNG).Conclusions These findings indicated that feeding L.plantarum 24-7 inoculated alfalfa silage not only improved rumen fermentation and milk quality in lactating dairy goats but also boosted their immunity and antioxidant status by modulating the expression of several genes related to antioxidant and inflammation in the mammary gland.

Dietary manganese supplementation inhibits abdominal fat deposition possibly by regulating gene expression and enzyme activity involved in lipid metabolism in the abdominal fat of broilers

Excessive abdominal fat deposition seriously restricts the production efficiency of broilers.Several studies found that dietary supplemental manganese(Mn)could effectively reduce the abdominal fat deposition of broilers,but the underlying mechanisms remain unclear.The present study aimed to investigate the effect of dietary supplementation with the inorganic or organic Mn on abdominal fat deposition,and enzyme activity and gene expression involved in lipid metabolism in the abdominal fat of male or female broilers.A total of 4201-d-old AA broilers(half males and half females)were randomly allotted by body weight and gender to 1 of 6 treatments with 10 replicates cages of 7 chicks per cage in a completely randomized design involving a 3(dietary Mn addition)×2(gender)factorial arrangement.Male or female broilers were fed with the Mn-unsupplemented basal diets containing 17.52 mg Mn kg^(-1)(d 1-21)and 15.62 mg Mn kg^(-1)(d 22-42)by analysis or the basal diets supplemented with 110 mg Mn kg^(-1)(d 1-21)and 80 mg Mn kg^(-1)(d 22-42)as either the Mn sulfate or the Mn proteinate with moderate chelation strength(Mn-Prot M)for 42 d.The results showed that the interaction between dietary Mn addition and gender had no impact(P>0.05)on any of the measured parameters;abdominal fat percentage of broilers was decreased(P<0.003)by Mn addition;Mn addition increased(P<0.004)adipose triglyceride lipase(ATGL)activity,while Mn-Prot M decreased(P<0.002)the fatty acid synthase(FAS)activity in the abdominal fat of broilers compared to the control;Mn addition decreased(P<0.009)diacylglycerol acyltransferase 2(DGAT2)mRNA expression level and peroxisome proliferator-activated receptor γ(PPARγ)mRNA and protein expression levels,but up-regulated(P<0.05)the ATGL mRNA and protein expression levels in the abdominal fat of broilers.It was concluded that dietary supplementation with Mn inhibited the abdominal fat deposition of broilers possibly via decreasing the expression of PPARγand DGAT2 as well as increasing the expression and activity of ATGL in the abdominal fat of broilers,and Mn-Prot M was more effective in inhibiting the FAS acitivity.

Effects of pyraclostrobin on growth,oxidative stress,and gene expression in relation to stress and ATP-binding cassette transporters in Tetrahymena thermophila

Pyraclostrobin(PYR),a widely used fungicide,has negative effects on fish and algae,but its toxicity in protozoa remains unclear.In this study,the effects of PYR on the growth,oxidative stress,and gene expression related to stress and ATP-binding cassette(ABC)transporters in Tetrahymena thermophila were investigated.The result showed that the 96-h IC_(50)of PYR against T.thermophila was 17.2 mg/L.Moreover,PYR inhibited the growth of T.thermophila in concentration-or time-dependent manner.A morphological study revealed that the shape and size of T.thermophila changed,and damage of cell membrane surface was observed by scanning electron microscopy after 96 h of PYR exposure.The activities of superoxide dismutase(SOD)and catalase(CAT)increased throughout the experiment.In contrast,the glutathione(GSH)content was increased at 24 h and 48 h of exposure and decreased at 96 h.Moreover,a significant increase in malondialdehyde(MDA)level was observed in T.thermophila after96 h of exposure.Furthermore,PYR upregulated the HSP703,HSP705,GPx2,and ABAC15 gene expression in the 0.1–5-mg/L groups and downregulated the HSP704,HSP90,TGR,and ABCC52 mRNA levels at 96 h of exposure.These results suggest that PYR may exert adverse effects on T.thermophila by inducing oxidative stress and changing the gene expression related to ABC transporters and stress,which may enrich the understanding of the toxicity mechanism of PYR in aquatic organisms and provide reference data for aquatic ecological risk assessments.

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

Gene expression analysis of cytokines and MMPs in melatonin and rhBMP-2 enhanced bone remodeling

BACKGROUND In the medical and dental fields,there is a need for studies of new therapeutic approaches for the treatment of bone defects that cause extensive bone loss.Melatonin may be an important endogenous biological factor for bone remodeling,and growth factors may enhance the repair process.AIM To evaluate the gene expression of cytokines(IL-1β,IL-6,IL-10 and TNF-α),markers of osteoclastogenesis(RANK,RANKL and OPG)and MMPs(MMP-1,MMP-2,MMP-8 and MMP-13)from the treatment of melatonin associated with an osteogenic membrane and rhBMP-2 on the recovery of a bone injury.METHODS Sixty-four rats were used and divided into 9 experimental groups and were formed according to the treatment carried out in the region of the bone lesion,which varied between the combination of 1,10 and 100μmol/L of melatonin.Gene Expression analysis was performed using real time-PCR by reading the concentration of total RNA and reverse transcription.RESULTS There were differences between groups when compared with clot or scaffold control,and improvement with a higher concentration of melatonin or rhBMP-2.The combination melatonin(1μg)with 5μg of rhBMP-2,using the guided bone regeneration technique,demonstrated some effects,albeit mild,on bone repair of critical bone defects.CONCLUSION This indicates that the approach for administering these substances needs to be reassessed,with the goal of ensuring their direct application to the affected area.Therefore,future research must be carried out,seeking to produce materials with these ideal characteristics.

Effect of Chuanzhifang component (ZGC) on macrophage inflammatory injury based on whole gene expression profile

Objective: The effect of Chuanzhi Fang (ZGC) on the whole genome expression profile of RAW264.7 cells activated by lipopolysaccharide (LPS) was analyzed, and to explore the possible mechanism of action and core target of this formula on macrophage inflammatory injury at the overall level. Methods: A model of LPS-induced inflammation in RAW264.7 cells was constructed, and the effect of ZGC intervention on the genome-wide expression of inflammatory macrophages 3was examined by gene microarray technology, GO/KEGG enrichment analysis was performed for significantly differentially expressed genes among each group. Results: The results of genome-wide expression profiling microarray analysis showed that the ZGC intervention group upregulated the expression of 5 genes including C4bp and inhibited the expression of 22 genes including Mgat3, Psma6, and Siglecg relative to the LPS model group. KEGG signaling pathway analysis results showed that ZGC mainly acted through cytokine receptor interaction and the C-type lectin receptor signaling pathway. Conclusion: ZGC can interfere with the abnormal expression of 27 genes in inflammatory macrophages, and the related genes may exert corresponding anti-inflammatory effects by affecting cytokine receptor interactions, C-type lectin receptor signaling pathway, and TLR4/ NF-κB signaling pathway.

Decoding Retinoblastoma: Differential Gene Expression

Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study leverages computational tools to dissect the differential gene expression profiles in retinoblastoma. Methods: Employing an in silico approach, we analyzed gene expression data from public repositories by applying rigorous statistical models, including limma and de seq 2, for identifying differentially expressed genes DEGs. Our findings were validated through cross-referencing with independent datasets and existing literature. We further employed functional annotation and pathway analysis to elucidate the biological significance of these DEGs. Results: Our computational analysis confirmed the dysregulation of key retinoblastoma-associated genes. In comparison to normal retinal tissue, RB1 exhibited a 2.5-fold increase in expression (adjusted p Conclusions: Our analysis reinforces the critical genetic alterations known in retinoblastoma and unveils new avenues for research into the disease’s molecular basis. The discovery of chemoresistance markers and immune-related genes opens potential pathways for personalized treatment strategies. The study’s outcomes emphasize the power of in silico analyses in unraveling complex cancer genomics.

Prediction of Lung Cancer Stage Using Tumor Gene Expression Data

Lung cancer remains a significant global health challenge and identifying lung cancer at an early stage is essential for enhancing patient outcomes. The study focuses on developing and optimizing gene expression-based models for classifying cancer types using machine learning techniques. By applying Log2 normalization to gene expression data and conducting Wilcoxon rank sum tests, the researchers employed various classifiers and Incremental Feature Selection (IFS) strategies. The study culminated in two optimized models using the XGBoost classifier, comprising 10 and 74 genes respectively. The 10-gene model, due to its simplicity, is proposed for easier clinical implementation, whereas the 74-gene model exhibited superior performance in terms of Specificity, AUC (Area Under the Curve), and Precision. These models were evaluated based on their sensitivity, AUC, and specificity, aiming to achieve high sensitivity and AUC while maintaining reasonable specificity.

In Vivo Improvements in Facial Appearance and in Vitro Changes in Gene Expression Using a Topical Formulation Designed to Repair Environmentally Induced DNA Damage

Background: While sunscreen has been accepted as a mainline defence against photodamage from ultraviolet, visible light and near-infrared radiation, there appears to be a lack of research into photorepair. The concept of protecting the skin during the day and repairing cellular damage at night is intuitive, yet specific strategies revolving around combinations of proven reparative active ingredients remain unelucidated. Purpose: To investigate the efficacy of a solar repair Formulation following ultraviolet and environmental exposure in order to improve overall skin health and appearance through three hypotheses: The Formulation increases expression of DNA repair mechanisms markers;The Formulation enhances overall skin appearance through reducing signs of inflammation, elevating hydration, reinforcing skin firmness and amplifying radiance;In-Vivo efficacy test results are aligned with measured gene expression changes. Methods: The Formulation (#6NIC1.V1.1-1) was tested for: In-vitro LDH cytotoxicity activity, In-vitro qPCR gene expression with and without ultraviolet exposure on a reconstructed 3-dimensional skin model, and In-Vivo efficacy study on a panel of 22 participants objectively and subjectively. Results: Skin radiance, firmness, hydration, redness, and inflammation are significantly improved after In-Vivo skin exposure to the Formulation and environmental challenges such as ultraviolet radiation. These outcomes were confirmed by in-vitro genetic testing on a reconstructed human skin model. Conclusion: The studies allowed us to identify and group results in four main skin functions that were significantly enhanced following the application of the Formulation: firmness, hydration, radiance and soothing.

Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus

BACKGROUND Breast cancer is regarded as a highly malignant neoplasm in the female population,posing a significant risk to women’s overall well-being.The prevalence of breast cancer has been observed to rise in China,accompanied by an earlier age of onset when compared to Western countries.Breast cancer continues to be a prominent contributor to cancer-related mortality and morbidity among women,primarily due to its limited responsiveness to conventional treatment modalities.The diagnostic process is challenging due to the presence of non-specific clinical manifestations and the suboptimal precision of conventional diagnostic tests.There is a prevailing uncertainty regarding the most effective screening method and target populations,as well as the specificities and execution of screening programs.AIM To identify diagnostic and prognostic biomarkers for breast cancer.METHODS Overlapping differentially expressed genes were screened based on Gene Expression Omnibus(GSE36765,GSE10810,and GSE20086)and The Cancer Genome Atlas datasets.A protein-protein interaction network was applied to excavate the hub genes among these differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses,as well as gene set enrichment analyses,were conducted to examine the functions of these genes and their potential mechanisms in the development of breast cancer.For clarification of the diagnostic and prognostic roles of these genes,Kaplan–Mei-er and Cox proportional hazards analyses were conducted.RESULTS This study demonstrated that calreticulin,heat shock protein family B member 1,insulin-like growth Factor 1,interleukin-1 receptor 1,Krüppel-like factor 4,suppressor of cytokine signaling 3,and triosephosphate isomerase 1 are potential diagnostic biomarkers of breast cancer as well as potential treatment targets with clinical implications.CONCLUSION The screening of biomarkers is of guiding significance for the diagnosis and prognosis of the diseases.

Metabolomics and gene expression levels reveal the positive effects of teaseed oil on lifespan and aging process in Caenorhabditis elegans

As an irreplaceable dietary constituent,lipids play a vital important role in health,but their effects on aging process and longevity are still not well known yet.In this paper,the metabolic profiling and gene expression levels of Caenorhabditis elegans were investigated to explore the effects of different edible oils on senescence and lifespan.The results showed that teaseed oil(TO)could prolong the life expectancy and slow down the aging process of C.elegans.Compared to the control group,the intake of lard oil(LO)and TO increased the expression levels of genes related to inhibition of protein aggregation(akt-1,daf-16,hsf-1,hsp-16.2)and lipid metabolism(daf-7,daf-1,mdt-15,lipl-4,fat-5,fat-6,fat-7),with a more significant alteration in TO group.Metabolomics revealed that palm oil can upregulated plenty of fatty acids(palmitic acid,stearic acid,tetracosanoic acid),together with some amino acids(tryptophan,L-aspartate,L-valine)and carbohydrate(D-glucose),while the trend was opposite in TO group.Besides,moderate-to-strong correlations were found between differential metabolites and changed genes.In general,this paper claimed that TO could prolong lifespan and slow down aging process via regulating the lipids,amino acids and carbohydrates metabolism.

A true triaxial strength criterion for rocks by gene expression programming

Rock strength is a crucial factor to consider when designing and constructing underground projects.This study utilizes a gene expression programming(GEP)algorithm-based model to predict the true triaxial strength of rocks,taking into account the influence of rock genesis on their mechanical behavior during the model building process.A true triaxial strength criterion based on the GEP model for igneous,metamorphic and magmatic rocks was obtained by training the model using collected data.Compared to the modified Weibols-Cook criterion,the modified Mohr-Coulomb criterion,and the modified Lade criterion,the strength criterion based on the GEP model exhibits superior prediction accuracy performance.The strength criterion based on the GEP model has better performance in R2,RMSE and MAPE for the data set used in this study.Furthermore,the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types.Compared to the existing strength criterion based on the genetic programming(GP)model,the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength(s1)with intermediate principal stress(s2).Finally,based on the Sobol sensitivity analysis technique,the effects of the parameters of the three obtained strength criteria on the true triaxial strength of the rock are analysed.In general,the proposed strength criterion exhibits superior performance in terms of both accuracy and stability of prediction results.

Insecticidal Potential of α-Pinene and β-Caryophyllene against Myzus persicae and Their Impacts on Gene Expression

Myzus persicae(M.persicae)is now considered a threat to agricultural crops due to economic losses.Numerous synthetic insecticides applied every year against M.persicae,are reported to be unsafe for environment,humans,and beneficial insects.Furthermore,several species of Myzus have been found to develop resistance due to over application of these insecticides.Therefore,it is required to find some novel insecticide that would be safe for the environment as well as for humans.In the current study,two major pure constituentsα-pinene andβ-caryophyllene were evaluated for their insecticidal potential against M.persicae using a fumigant toxicity assay.Furthermore,impact ofα-pinene andβ-caryophyllene on expression of five different genes,e.g.,HSP 60,FPPS I,OSD,TOL and ANT responsible for reproduction,dispersion,and growth of M.persicae has also been investigated.To perform fumigant toxicity assay,five different concentrations(3.5,4,4.5,5 and 6μL L−1)ofα-pinene andβ-caryophyllene were prepared.Lethal concentration(LC)was calculated,and gene expression studies were executed through qRT PCR at LC30 ofα-pinene andβ-caryophyllene.Both constituents demonstrated excellent fumigant toxicity effects against M.persicae at all five concentrations.However,α-pinene shows significantly better results(98%)as compared toβ-caryophyllene(80%)after 72 h at 6μL L−1 of dose.The highest upregulation in expression was demonstrated at LC30 dose ofα-pinene in five in three out of five genes understudy(TOL,ANT,and FPPS I).Conversely,two genes HSP 60 and OSD demonstrated downregulation at LC30 dose ofβ-caryophyllene.Conclusively,our results highlighted the promising insecticidal potential of both compoundsα-pinene andβ-caryophylleneby interfering with the reproduction and development related processes in M.persicae,allowing us to recommend the phytoconstituents under investigation as an ecofriendly alternative to synthetic insecticides.

Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules.

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.

Selenium: A Game Changer in Plant Development, Growth, and Stress Tolerance, via the Modulation in Gene Expression and Secondary Metabolite Biosynthesis

The presence of selenium(Se)is not widely established as crucial for crops,although it is commonly recognized as an important nutrient for animals as well as humans.Even so,it is inevitably accepted that Se usually contributes positively to the life cycle of plants.Previousfindings suggested that small amounts of Se seem to have a productive role in growth and production.As a result,Se is assumed to function in multiple ways,primarily by influencing a variety of biochemical and physiological functions.Also,Se also acts as a plant antioxidant and pro-oxidant and confers tolerance against different abiotic stresses,including salinity,drought,extreme temperature,and toxic metals/metalloids stresses.It reflects a defensive barrier against stress by increasing chlorophyll content synthesis,photosynthesis,oxygen supply,osmoprotectant concentration,and secondary metabolite acquisition.One other crucial role of Se is its ability to strengthen antioxidant performance in plants,thereby decreasing the concentration of reactive-oxygen-species(ROS).Furthermore,Se generates and modifies genes and proteins that respond situationally to stress,and the presence of high Se concentrations in the growth-medium can cause phytotoxic conditions via excessive ROS production,and through pro-oxidative Se occurrence,suppression of chlorophyll contents in the biosynthetic pathway,and the inhibition of plant developmental and normal physiological functions.Like a phytofortifier,the correct amount of Se can indeed enhance the nutrient quality of both crop and fodder production.Furthermore,crops have naturally developed ways to combat Se-deficiency and Se-toxicity.The current review focuses on recent advances in understanding the dynamics of Se,the positive and negative roles of Se in crop management,and its efficiency in countering abiotic stress.

Analysis of differentially expressed genes related to cerebral ischaemia in young rats based on the Gene Expression Omnibus database

BACKGROUND The incidence rate of cerebral infarction in young people is increasing day by day,the age of onset tends to be younger,and its internal pathogenesis and mechanism are very complicated,which leads to greater difficulties in treatment.Therefore,it is essential to analyze the key pathway that affects the onset of cerebral infarction in young people from the perspective of genetics.AIM To compare the differentially expressed genes in the brain tissue of young and aged rats with middle cerebral artery occlusion and to analyse their effect on the key signalling pathway involved in the development of cerebral ischaemia in young rats.METHODS The Gene Expression Omnibus 2R online analysis tool was used to analyse the differentially expressed genes in the GSE166162 dataset regarding the development of cerebral ischaemia in young and aged groups of rats.DAVID 6.8 software was further used to filter the differentially expressed genes.These genes were subjected to Gene Ontology(GO)function analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis to determine the key gene pathway that affects the occurrence of cerebral ischaemia in young rats.RESULTS Thirty-five differentially expressed genes(such as Igf2,Col1a2,and Sfrp1)were obtained;73 GO enrichment analysis pathways are mainly involved in biological processes such as drug response,amino acid stimulation response,blood vessel development,various signalling pathways,and enzyme regulation.They are involved in molecular functions such as drug binding,protein binding,dopamine binding,metal ion binding,and dopamine neurotransmitter receptor activity.KEGG pathway enrichment analysis showed a significantly enriched pathway:The cyclic adenosine monophosphate(c-AMP)signalling pathway.CONCLUSION The c-AMP signalling pathway might be the key pathway in the intervention of cerebral infarction in young people.

Effects of Manganese on the Antioxidant System and Related Gene Expression Levels in the “Hong Yang” Kiwifruit Seedlings

To explore how manganese affects the antioxidant system and the expression levels of related genes of“Hong yang”seedlings,the leaves of its tissue cultured seedlings were taken as test materials,and single factor treatment was performed by changing the manganese chloride(MnCl_(2)·4H_(2)O)solution concentration when spraying the leaves.The expression levels of Mn-SOD,POD64 and POD27 genes in leaves were quantitatively analyzed by real-time quantitative PCR(qRT-PCR)at different determination times.Meanwhile,the contents of malondial-dehyde(MDA),hydrogen peroxide(H_(2)O_(2)),the activities of antioxidant enzymes,including catalase(CAT),peroxidase(POD),and superoxide dismutase(SOD).The results showed that the SOD,CAT,POD,ascorbate peroxidase(APX),and reduced glutathione(GSH)activities in leaves were the highest at 12 h post-treatment with 50μM MnCl_(2)·4H_(2)O.Furthermore,the contents of MDA and H_(2)O_(2) in leaves also peaked when the concentration of H_(2)O_(2) is 50μM,which is the minimum value.Additionally at 50μM Mn^(2+),the Mn-SOD and POD27 expression was up-regulated as compared to the control,which promoted the expression of their respective enzyme activities.However,POD64 expression increased with the increasing Mn^(2+) concentration.Therefore,50μM is the optimal concentration of Mn when exogenously applied on“Hong yang”,which improve the antioxidant enzyme activity and regulate the plant’s physiological and biochemical functions.

Altered Liver Gene Expression Due to Hypertension and Age in Rats

Hypertension and metabolic syndrome, both of which increase with age, are multifactorial disorders. Their etiology is complex, making it challenging to isolate involved genes. This study aimed to characterize the hepatic gene expression in spontaneously hypertensive rats (SHR) at different ages. Blood pressure in SHR was determined by tail-cuff method at one and three months of age. Hepatic RNA was isolated and gene expression was compared using microarrays. Comparison between SHR and normotensive rats revealed significant variation in gene expression: 98 genes were upregulated and 122 were downregulated in SHR;while 88 genes were upregulated and 139 genes were downregulated in age-matched normotensive rats. Furthermore, within the SHR group, 110 genes were found to be upregulated and 168 genes downregulated across different ages. Analyses via the Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathways revealed that several genes are potentially implicated in both, hypertension and metabolic syndrome. The results suggest that SHR display variations in gene expression due to aging, and when compared to normotensive rats. These variations could contribute to the development of hypertension and metabolic syndrome. Microarray studies involving older rats are necessary to further validate these findings.

Integrated analysis of gene expression profiles reveals prognostic biomarkers for immunotherapy in cancer

Tumor immunotherapy has emerged as a promising method in cancer treatment,but patient responses vary,necessitating personalized strategies and prognostic biomarkers.This study aimed to identify prognostic factors and construct a predictive model for patient survival outcomes and immunotherapy response.We curated six immunotherapy datasets representing diverse cancer types and treatment regimens.After data preprocessing,patients were stratified based on immunotherapy response.Differential gene expression analysis identified 22 genes consistently dysregulated across multiple datasets.Functional analysis provided critical insights,highlighting the enrichment of these dysregulated genes in immune response pathways and tumor microenvironment-related processes.To create a robust prognostic model,we meticulously employed a multistep approach.Initially,the identified 22 genes underwent rigorous univariate Cox regression analysis to evaluate their individual associations with patient survival outcomes.Genes showing statistical significance(p-values<0.05)at this stage advanced to the subsequent multivariate Cox regression analysis,which aimed to address potential confounding factors and collinearity among genes.From this analysis,we ultimately identified four key genes—ST6GALNAC2,SNORA65,MFAP2,and CDKN2B—that were significantly associated with patient survival outcomes.Incorporating these four key genes along with their corresponding coefficients,we constructed a predictive model.This model’s efficacy was validated through extensive Cox regression analyses,demonstrating its robustness in predicting patient survival outcomes.Furthermore,our model exhibited promising predictive capability for immunotherapy response,providing a potential tool for anticipating treatment efficacy.These findings provide insights into immunotherapy response mechanisms and suggest potential prognostic biomarkers for personalized treatment.Our study contributes to advancing cancer immunotherapy and personalized medicine.