Objective To observe the effect of BMSCs on the cardiac function in diabetes mellitus (DM) rats through injecting BMSCs into the ventricular wall of the diabetic rats and investigate its mechanism. Methods BMSCs isolated from male SD rats (3-4 months old) were cultured in vitro, and the cells at passage 5 underwent DAPI label ing. Thirty clean grade SD inbred strain male rats weighing about 250 g were randomized into the normal control group (group A), the DM group (group B), and the cell transplantation group (group C). The rats in groups B and C received high fat forage for 4 weeks and the intraperitoneal injection of 30 mg/kg streptozotocin to made the experimental model of type II DM. PBS and DAPI-labeledpassage 5 BMSCs (1 × 105/μL, 160 μL) were injected into the ventricular wall of the rats in groups B and C, respectively. After feeding those rats with high fat forage for another 8 weeks, the apoptosis of myocardial cells was detected by TUNEL, the cardiac function was evaluated with multi-channel physiology recorder, the myocardium APPL1 protein expression was detected by Western blot and immunohistochemistry test, and the NO content was detected by nitrate reductase method. Group C underwent all those tests 16 weeks after taking basic forage. Results In group A, the apoptosis rate was 6.14% ± 0.02%, the AAPL1 level was 2.79 ± 0.32, left ventricular -dP/dt (LV-dP/dt) was (613.27 ± 125.36) mm Hg/s (1 mm Hg=0.133 kPa), the left ventricular end-diastol ic pressure (LVEDP) was (10.06 ± 3.24) mm Hg, and the NO content was (91.54 ± 6.15) nmol/mL. In group B, the apoptosis rate was 45.71% ± 0.04%, the AAPL1 level 1.08 ± 0.24 decreased significantly when compared with group A, the LVdP/ dt was (437.58 ± 117.58) mm Hg/s, the LVEDP was (17.89 ± 2.35) mm Hg, and the NO content was (38.91±8.67) nmol/mL. In group C, the apoptosis rate was 27.43% ± 0.03%, the APPL1 expression level was 2.03 ± 0.22, the LV -dP/dt was (559.38 ± 97.37) mm Hg/ s, the LVEDP was (12.55 ± 2.87) mm Hg, and the NO content was (138.79 ± 7.23) nmol/ mL. For the above mentioned parameters, there was significant difference between group A and group B (P lt; 0.05), and between group B and group C (P lt; 0.05). Conclusion BMSCs transplantation can improve the cardiac function of diabetic rats. Its possible mechanismmay be related to the activation of APPL1 signaling pathway and the increase of NO content.
In order to improve the epilepsy management and treatment of Hebei province, improve the life quality of epilepsy patients. Hebei Association Against Epilepsy start a multicenter-clinical trial about the diagnosis, medicine treatment and effect of epilepsy through mobile phone APP. The data collected by health management APP shows that the diagnosis rate of epilepsy syndrome has enhanced from 21% to 39% within six months; also 80.4% of patients have got seizure-free within the first month of treatment. Therefore, the diagnosis and treatment of epilepsy in Hebei province has been improved. However, only 46% of adult patients have been hospitalized within the first 2 years of seizure onset. Therefore, patients need further education about epilepsy in the future. In this trial, the daily management of patients by doctors has come true through the use of mobile phone APP. Through the mobile phone APP, doctors achieved the real-time supervision of disease progress and adjustment of the treatment. This trial provide evidence for future treatment and daily management of epilepsy patients.
Objective To systematically review the effect of mobile phone management applications (APP) on biological and biochemical parameters in adults with type 2 diabetes mellitus (T2DM). Methods We electronically searched databases including PubMed, EMbase, The Cochrane Library, Web of Science, CINAHL, PsycINFO, WanFang Data, CBM, CNKI for randomized controlled trials compared mobile phone APP as interventions for managing T2DM with traditional methods from inception to September 30th 2016. Two researchers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was conducted using Stata 12.0 software. Results A total of 10 studies including 1 030 participants (563 in the App group and 467 in the traditional group) were included. The results of meta-analysis showed that the mobile phone app-based interventions group were superior to the control group in HbA1c (MD=–0.43, 95% CI –0.65 to –0.22, P<0.001), systolic blood pressure (MD=–2.53, 95% CI –4.89 to –0.17,P<0.05), triglyceride (SMD=–0.24, 95% CI –0.42 to –0.06,P<0.05) and waist circumference (MD=–1.57, 95% CI –2.65 to –0.48,P<0.05). No significant different were found on any other related results between two groups. Conclusion Mobile phone apps aimed at diabetes management can improve HbA1c, systolic blood pressure, triglyceride, and waist circumference for patients with T2DM. Due to the limited quality and quantity of the included studies, the above conclusions are needed more high quality studies to verify.
Here, we report a genome-wide survey of immunoglobulin light chain (IGL) genes of torafugu (Takifugu rubripes) revealing multi-clusters spanning three separate chromosomes (v5 assembly) and 45 scaffolds (v4 assembly). Conventional sequence similarity searches and motif scanning approaches based on recombination signal sequence (RSS) motifs were used. We found that three IGL isotypes (L1, L2, and L3) exist in torafugu and that several loci for each isotype are present. The transcriptional orientations of the variable IGL (VL) segments were found to be either the same (in the L2 isotype) or opposite (in the L1 and L3 isotypes) to the IGL joining (JL) and constant (CL) segments, suggesting they can undergo rearrangement by deletion or inversion when expressed. Alignments of expressed sequence tags (ESTs) to corresponding germline gene segments revealed expression of the three IGL isotypes in torafugu. Taken together, our findings provide a genomic framework for torafugu IGL genes and show that the IG diversity of this species could be attributed to at least three distinct chromosomal regions.
ObjectiveTo systematically review the efficacy of mobile health (mHealth) apps on weight loss.MethodsWeb of Science, PubMed, CNKI, WanFang Data and CBM databases were electronically searched to collect randomized controlled trials (RCTs) on the efficacy of mHealth APPs on weight loss from inception to October 18, 2019. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies. Meta-analysis was then performed using RevMan 5.3 software.ResultsA total of 12 RCTs involving 1 074 patients were included. The results of meta-analysis showed that the apps group was superior to the control group in body weight (MD=−1.88, 95%CI −3.23 to −0.53, P=0.006). There was a decreasing trend of body mass index (BMI) in apps group (MD=−0.55, 95%CI −1.09 to 0.00, P=0.05).ConclusionsCurrent evidence shows that mHealth app can increase the efficacy of weight loss. Due to limited quality and quantity of the included studies, more highquality studies are required to verify above conclusions.
ObjectiveTo explore the methods of data management and statistical analysis for longitudinal big data collected from mobile health management applications (APP). MethodsThe data management process and statistical analysis method were proposed by summarizing the characteristics of the data from mobile health management APPs. The methods would be clarified by a practical case: an APP recording female menstruation. ResultsThe data from health management APPs belong to longitudinal big data and the original record of the APP should be reprocessed or computed before conducting statistical analysis. A two-step data cleaning procedure was suggested for data management of the original records and reprocessed data, and longitudinal models such as mixed models was recommended for statistical analysis. ConclusionsThe data from health management APPs could be used for medical research via specific data management and statistical analysis after removing suspicious data. Cloud computing could be a viable method to improve efficiency of the big data analysis of health management APPs.