A novel procedure for absolute real-time quantification of gene expression patterns

<p>Abstract</p> <p>Background</p> <p>Temporal and tissue-specific patterns of gene expression play important roles in functionality of a biological system. Real-time quantitative polymerase chain reaction (qPCR) technique has been widely applied to single gene expressions, but its potential has not been fully released as most results have been obtained as fold changes relative to control conditions. Absolute quantification of transcripts as an alternative method has yet to gain popularity because of unresolved issues.</p> <p>Results</p> <p>We propose a solution here with a novel procedure, which may accurately quantify the total cDNA conventionally prepared from a biological sample at the resolution of ~70 pg/μl, and reliably estimate the absolute numbers of transcripts in a picogram of cDNA. In comparison to the relative quantification, cDNA-based absolute (CBA) qPCR method is found to be more sensitive to gene expression variations caused by factors such as developmental and environmental variations. If the number of target transcript copies is further normalized by reference transcripts, cell-level variation pattern of the target gene expression may also be detectable during a developmental process, as observed here in cases across species (<it>Ipomoea purpurea, Nicotiana benthamiana</it>) and tissues (petals and leaves).</p> <p>Conclusion</p> <p>By allowing direct comparisons of results across experiments, the new procedure opens a window to make inferences of gene expression patterns across a broad spectrum of living systems and tissues. Such comparisons are urgently needed for biological interpretations of gene expression variations in diverse cells.</p

Enhancing the efficiency of human pancreatic islet dissociation

Over half a million children worldwide are affected by type 1 diabetes, an autoimmune disease characterized by the destruction of insulin-producing pancreatic beta cells. Islet transplantation is a treatment that is currently limited by the lack of vascular network to support large islets post-transplantation. A promising proposal is to disperse native islets into single-cell suspensions and re-aggregate them into smaller, uniform “pseudo-islets”. Substantial cell loss during islet dispersion, however, remains an important obstacle that limits the yield of pseudo-islet aggregates, especially considering the scarcity of donor islets. To optimize the dissociation protocol, we experimented with different cell dissociation reagents, concentrations, and times in order to establish standards for future pseudo-islet formation procedure.Isolated human islets were dissociated using Trypsin, TrypLE, Accutase, Accumax, and Dispase. These dissociation reagents were identified through literature and the concentrations as well as dissociation times used were in ranges previously outlined. Cell counts of viable cells were recorded using Trypan Blue and PicoGreen DNA assay to quantify cell loss during islet dispersion, filtration and post-culture. Assessment of the viability of the re-aggregated pseudo-islets post-culture was performed using the Alamar Blue assay.Preliminary results showed the potential for 5.8-fold increase in cell recovery which provides evidence of the significant need to optimize the dissociation protocol. TrypLE showed the highest recovery of cells both post-dissociation and filtration. Results from the project are promising and further investigations will allow the results to become applicable to clinical trials. Improving the recovery and quality of dissociated islet cells will directly help increase the number of treatable patients from the limited supply of donor islets

Identification of related long non-coding RNAs and mRNAs in subclinical hypothyroidism complicated with type 2 diabetes by transcriptome analysis — a preliminary study

Introduction: The pathology mechanism of subclinical hypothyroidism and subclinical hypothyroidism complicated with type 2 diabetes remained uncertain. We aimed to find potential related long non-coding RNAs (lncRNAs) and mRNAs in the above diseases. Material and methods: Transcriptome sequencing was performed in three patients with subclinical hypothyroidism (S), three patients with subclinical hypothyroidism complicated with type 2 diabetes (SD), and three healthy controls (N). Differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) were screened in S vs. N, SD vs. N, and SD vs. S group, and the nearby and co-expressed DEmRNAs of DElncRNAs were screened in S vs. N and SD vs. N. Moreover, functional analysis of DEmRNAs was then performed by Metascape. Results: In total, 465, 1058, and 943 DEmRNAs were obtained in S vs. N, SD vs. N, SD vs. S, respectively, and 191 overlapping genes were obtained in S vs. N and SD vs. N group. Among which, LAIR2, PNMA6A, and SFRP2 were deduced to be involved in subclinical hypothyroidism, and GPR162, APOL4, and ANK1 were deduced to be associated with subclinical hypothyroidism complicated with type 2 diabetes. A total of 50, 100, and 88 DElncRNAs were obtained in S vs. N, SD vs. N and SD vs. S, respectively. Combining with the interaction network of DElncRNA-DEmRNA, PAX8-AS1, co-expressed with KIR3DL1, was identified to function in subclinical hypothyroidism, and JHDM1D-AS1, co-expressed with ANK1, was deduced to play a role in subclinical hypothyroidism complicated with type 2 diabetes. Conclusions: Dysfunctional lncRNAs and mRNAs may be involved in the development of subclinical hypothyroidism and subclinical hypothyroidism complicated with type 2 diabetes.

Prevalence and Risk Factors Associated with Prehypertension among Young and Middle-Aged Health Check-Up Population in Guangzhou

Objective: To provide basic information and theories for prehypertension early intervention, a systematic analysis of the epidemic status and risk factors among young and middle-aged was carried out here. Methods: This study relied on the data bank of a health check-up population of a class a tertiary general hospital in Guangdong province in 2015. Total 9540 young and middle-aged adults were enrolled, and 733 people were included to find out the effect with lifestyle in these crowd. Principal Components Analysis (PCA) of Factor (FA) was used to identify dietary patterns. The logistic regression model was used to find the risk factors of prehypertension. Results: Among 9540 young and middle-aged cases, the incidence of prehypertension was 36.6%. Moreover, the average age, proportion of male gender, overweight, FBG (fasting blood glucose), dyslipidemia, and hyperuricemia were significantly higher in the prehypertension group than in the optimal BP group. Multivariate logistic regression analysis indicated that age, total cholesterol, triglycerides, uric acid, body mass index and HR (heart rate) were risk factors, and female was a protective factor for prehypertension. Among 733 cases, the incidence of prehypertension was 35.1%. The proportion of smoking, drinking, physical workers, moderate and severe physical activity, and the intake of meat, dietary energy were significantly higher in the prehypertension group than in the optimal BP group. Dietary patterns included “meat model”, “spice model”, “main vegeTables model” and “high protein model”. Multivariate logistic regression analysis indicated that age, drinking were risk factors for prehypertension, while dietary milk intake, dietary magnesium intake were protective factors. Conclusions: Prehypertension is highly prevalent in Guangzhou. However, education about effective lifestyle modifications as an alcohol limit, increasing the intake of dairy products, and magnesium may intervene in the development of prehypertension. But how to develop targeted interventions for such groups need to be further explored. The present study would lay the theoretical foundation and basic data for the next step

Vector-valued Privacy-Preserving Average Consensus

Achieving average consensus without disclosing sensitive information can be a critical concern for multi-agent coordination. This paper examines privacy-preserving average consensus (PPAC) for vector-valued multi-agent networks. In particular, a set of agents with vector-valued states aim to collaboratively reach an exact average consensus of their initial states, while each agent's initial state cannot be disclosed to other agents. We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state. Specifically, a novel distributed vector-valued PPAC algorithm is proposed by lifting the agent-state to higher-dimensional space and designing the associated matrix-weighted network with dynamic, low-rank, positive semi-definite coupling matrices to both conceal the vector-valued agent state and guarantee that the multi-agent network asymptotically converges to the average consensus. Essentially, the convergence analysis can be transformed into the average consensus problem on switching matrix-weighted networks. We show that the exact average consensus can be guaranteed and the initial agents' states can be kept private if each agent has at least one "legitimate" neighbor. The algorithm, involving only basic matrix operations, is computationally more efficient than cryptography-based approaches and can be implemented in a fully distributed manner without relying on a third party. Numerical simulation is provided to illustrate the effectiveness of the proposed algorithm

Research on the Model of Making a Price Match Based-on Automatic Negotiated Price for Electronic Commerce

The paper established a new sealed bargaining mechanism based on the electronic business negotiation model and considering the opaqueness of information on demand and supply. Using the supply function and demand function to analyze the behavior rule during the course of the price change, in the paper we established and proved a series of intersecting chord theorems about concave supply function and demand function, thus we got a transaction mechanism of negotiating prices that manufacturers and distributors submitted the supply and demand according to node gradually recursion algorithm after the first offer made by the e-commerce platform, And proved the negotiated price converged to the equilibrium price of supply and marketing

The relationship between IGF1 and the expression spectrum of miRNA in the placenta of preeclampsia patients

Objectives: Pre-eclampsia (PE) affects many women worldwide and remains the leading cause of morbidity and mortality in neonatal and maternal settings. Abnormal expression of placental microRNAs (miRNAs) may be associated with PE. Material and methods: This study was conducted to the relationship between IGF1 and the expression spectrum of miRNA in the placenta of preeclampsia patient. The expression of miRNA in placental tissue was compared between pre-eclampsia (n = 6) and normal pregnant women (n = 5) miRNA targets were studied by computer simulation and functional assays. The role of miRNA was verified in trophoblast cell lines by apoptosis assay and invasion assay. Results: There was a significant increase in miRNAs in the placenta of women with pre-eclampsia compared with patients with normal pregnancy. Luciferase assay confirmed direct regulation of miRNA. Conclusions: The expression of IGF1 and miRNA was significantly increased in the placenta of patients with pre-eclampsia

Night shifts, insomnia, anxiety, and depression among Chinese nurses during the COVID-19 pandemic remission period: A network approach

BackgroundThe outbreak of the COVID-19 pandemic imposed a heavy workload on nurses with more frequent night shifts, which led to higher levels of insomnia, depression, and anxiety among nurses. The study aimed to describe the symptom-symptom interaction of depression, anxiety, and insomnia among nurses and to evaluate the impact of night shifts on mental distress via a network model.MethodsWe recruited 4,188 nurses from six hospitals in December 2020. We used the Insomnia Severity Index, Patient Health Questionnaire-9, and Generalized Anxiety Disorder Scale-7 to assess insomnia, depression, and anxiety, respectively. We used the gaussian graphical model to estimate the network. Index expected influence and bridge expected influence was adapted to identify the central and bridge symptoms within the network. We assessed the impact of night shifts on mental distress and compared the network structure based on COVID-19 frontline experience.ResultsThe prevalence of depression, anxiety, and insomnia was 59, 46, and 55%, respectively. Nurses with night shifts were at a higher risk for the three mental disorders. “Sleep maintenance” was the central symptom. “Fatigue,” “Motor,” “Restlessness,” and “Feeling afraid” were bridge symptoms. Night shifts were strongly associated with sleep onset trouble. COVID-19 frontline experience did not affect the network structure.Conclusion“Sleep maintenance,” “Fatigue,” “Motor,” and “Restlessness” were important in maintaining the symptom network of anxiety, depression, and insomnia in nurses. Further interventions should prioritize these symptoms