ObjectiveTo set reference intervals of the levels of thyroid hormones among normal pregnant women without presence of thyroid antibodies during three trimesters of pregnancy in Quanzhou city, Fujian province.
MethodsA total of 490 pregnant women during 4-39 week pregnancy without presence of thyroid antibodies were enrolled in Quanzhou city, Fujian province. Levels of thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4) and thyroid peroxidase antibodies (TPO-Ab) were detected through the electrochemistry immunoassay (ECL) method. In addition, a total of 51 healthy women without pregnancy were enrolled to set the reference intervals of levels of thyroid hormones among normal pregnant women without presence of thyroid antibodies.
ResultsThe median levels of TPO-Ab were in the reference intervals provided by the pharmaceutical factory. Levels of FT3 and FT4 gradually decreased from the first to the third trimester (P < 0.01), levels of serum TSH gradually increasd from the first to the third trimester (P < 0.01). Compared with those of pregnant women, levels of thyroid hormone in normal non-pregnant women were higher in the first trimester, lower in the second and the third trimesters (P < 0.01). During three trimesters, the reference intervals of FT3 in the three trimesters were (first: 3.75 to 7.23; second 3.31 to 4.9; and third: 3.16 to 4.48 pmol/L); the reference intervals of FT4 were (first: 12.85 to 25.3; second: 12.03 to 20.14; and third: 11.02 to 19.43 pmol/L); and the reference intervals of TSH were (first: 0.01 to 3.79; second: 1.09 to 4.19; and third: 1.08 to 5.95 mIU/L), respectively.
ConclusionThrough this detection, we set the levels of thyroid hormones among normal pregnant women without presence of thyroid antibodies during three trimesters of pregnancy in Quanzhou city.
Objectives
To detect expressions of heat shock protein 70 (HSP70) and glial fibrillary acidic protein (GFAP) , and to estimate the post-injury interval after concussion of brain via the ratios of percentage of HSP70/GFAP-positive cells.
Methods
We established a brain concussion model of rat. Tissue levels of HSP70 and GFAP were determined by immunohistochemical staining at different time points after injury. Finally, the relationship between the ratio of percentage of HSP70/GFAP-positive cells and the post-injury interval was measured.
Results
The ratio of percentage of positive cells (increased from 7.15 to 11.73) and the percentage of HSP70-positive cells (P<0.05, compared with control group) increased, and the percentage of GFAP-positive cells did not change remarkably (P<0.05, compared with control group); the post-injury interval was between 0.5 hour and 3 hours. High ratio (>6.66) and high percentage of HSP70 and GFAP-positive cells (P<0.05, compared with control group) indicated the post-injury interval was between 3 and 12 hours. A low ratio (<6.66) and high percentage of HSP70 and GFAP-positive cells (P<0.05, compared with control group) suggested that the post-injury interval was later than 12 hours.
Conclusion
By analyzing the variation rule of the ratio of percentage positive cells after brain concussion, the post-injury interval after concussion of brain could be estimated.
Most statistical data in observational studies is expressed as the effect value and its 95% confidence interval (95% CI), which do not correspond to the data format used for traditional meta-analyses, so special data conversion is to be needed when Review Manager software is applied to do a meta-analysis for this type of data, which will make the operation complicated and cumbersome. In addition, Stata software is such a powerful statistical software that can be used directly to conduct a meta-analysis with the effect value and its 95% CI. Therefore, it is an indispensable statistical tool for meta-analysis in observational studies. And this study will give a brief introduction how to use Stata software to conduct a meta-analysis with effect value and its 95% CI based on the published meta-analysis data.
With the prevalence of cardiovascular diseases, the development of cardiac rehabilitation is an inevitable trend. Cardiac rehabilitation is a comprehensive and long-term plan including medical assessment, exercise prescription, correction of cardiovascular risk factors, education, counseling, and behavioral intervention. Evidence-based medical evidence confirms that cardiac rehabilitation plays an important role in the three level prevention of cardiovascular disease. In this paper, we searched the literature in recent 10 years to explain the current situation and future research direction of cardiac rehabilitation, and explore the best mode of cardiac rehabilitation.
ObjectiveTo systematically review the intervention effects of high-intensity interval training (HIIT) on weight loss and blood lipid metabolism in overweight/obese populations. MethodsThe computer conducted searches in the PubMed, Embase, Cochrane Library, Web of Science, CNKI, and WanFang Data database to collect randomized controlled trials (RCTs) related to HIIT and weight loss, fat reduction, and blood lipid metabolism in overweight/obese populations. The search was conducted from the inception of the databases to March 31, 2023. Two researchers independently conducted literature screening and data extraction. After evaluating the risk of bias of the included studies, a meta-analysis was performed using RevMan 5.4 software. ResultsA total of 19 RCTs, involving 595 overweight/obese participants, were included. The meta-analysis results showed that compared wtih the control group, HIIT interventions effectively reduced body weight (MD=?2.63, 95%CI ?4.04 to ?1.23, P<0.05), BMI (MD=?1.21, 95%CI ?1.95 to ?0.48, P<0.05), Fat% (MD=?1.66, 95%CI ?2.28 to ?1.04, P<0.05), TG (MD=?0.13, 95%CI ?0.25 to ?0.01, P=0.04), HDL (MD=0.14, 95%CI 0.05 to 0.23, P<0.05), and LDL (MD=?0.26, 95%CI ?0.39 to ?0.13, P<0.05) levels but did not improve TC (MD=?0.15, 95%CI ?0.36 to 0.06, P=0.15) levels. ConclusionHIIT intervention can effectively improve body weight, BMI, Fat%, TG, HDL, and LDL levels in overweight/obese populations, particularly showing a more pronounced improvement in lipid profiles among overweight/obese adolescents, but it does not reduce TC levels. This study demonstrates that HIIT may be an effective strategy to assist in weight loss and prevent cardiovascular diseases in overweight/obese populations, with potential for broader application.
Predicting the termination of paroxysmal atrial fibrillation (AF) may provide a signal to decide whether there is a need to intervene the AF timely. We proposed a novel RdR RR intervals scatter plot in our study. The abscissa of the RdR scatter plot was set to RR intervals and the ordinate was set as the difference between successive RR intervals. The RdR scatter plot includes information of RR intervals and difference between successive RR intervals, which captures more heart rate variability (HRV) information. By RdR scatter plot analysis of one minute RR intervals for 50 segments with non-terminating AF and immediately terminating AF, it was found that the points in RdR scatter plot of non-terminating AF were more decentralized than the ones of immediately terminating AF. By dividing the RdR scatter plot into uniform grids and counting the number of non-empty grids, non-terminating AF and immediately terminating AF segments were differentiated. By utilizing 49 RR intervals, for 20 segments of learning set, 17 segments were correctly detected, and for 30 segments of test set, 20 segments were detected. While utilizing 66 RR intervals, for 18 segments of learning set, 16 segments were correctly detected, and for 28 segments of test set, 20 segments were detected. The results demonstrated that during the last one minute before the termination of paroxysmal AF, the variance of the RR intervals and the difference of the neighboring two RR intervals became smaller. The termination of paroxysmal AF could be successfully predicted by utilizing the RdR scatter plot, while the predicting accuracy should be further improved.
It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.
In order to quantitatively analyze the morphology and period of pulse signals, a time-space analytical modeling and quantitative analysis method for pulse signals were proposed. Firstly, according to the production mechanism of the pulse signal, the pulse space-time analytical model was built after integrating the period and baseline of pulse signal into the analytical model, and the model mathematical expression and its 12 parameters were obtained for pulse wave quantification. Then, the model parameters estimation process based on the actual pulse signal was presented, and the optimization method, constraints and boundary conditions in parameter estimation were given. The spatial-temporal analytical modeling method was applied to the pulse waves of healthy subjects from the international standard physiological signal sub-database Fantasia of the PhysioNet in open-source, and we derived some changes in heartbeat rhythm and hemodynamic generated by aging and gender difference from the analytical models. The model parameters were employed as the input of some machine learning methods, e.g. random forest and probabilistic neural network, to classify the pulse waves by age and gender, and the results showed that random forest has the best classification performance with Kappa coefficients over 98%. Therefore, the space-time analytical modeling method proposed in this study can effectively quantify and analyze the pulse signal, which provides a theoretical basis and technical framework for some related applications based on pulse signals.
Missing data represent a general problem in many scientific fields, especially in medical survival analysis. Dealing with censored data, interpolation method is one of important methods. However, most of the interpolation methods replace the censored data with the exact data, which will distort the real distribution of the censored data and reduce the probability of the real data falling into the interpolation data. In order to solve this problem, we in this paper propose a nonparametric method of estimating the survival function of right-censored and interval-censored data and compare its performance to SC (self-consistent) algorithm. Comparing to the average interpolation and the nearest neighbor interpolation method, the proposed method in this paper replaces the right-censored data with the interval-censored data, and greatly improves the probability of the real data falling into imputation interval. Then it bases on the empirical distribution theory to estimate the survival function of right-censored and interval-censored data. The results of numerical examples and a real breast cancer data set demonstrated that the proposed method had higher accuracy and better robustness for the different proportion of the censored data. This paper provides a good method to compare the clinical treatments performance with estimation of the survival data of the patients. This provides some help to the medical survival data analysis.
ObjectiveTo investigate the effect of the interval between neoadjuvant chemoradiotherapy (nCRT) and surgery on the clinical outcome of esophageal cancer.MethodsPubMed and EMbase databases from inception to March 2018 were retrieved by computer. A random-effect model was used for all meta-analyses irrespective of heterogeneity. The meta-analysis was performed by RevMan5.3 software. The primary outcomes were operative mortality, incidence of anastomotic leakage, and overall survival; secondary outcomes were pathologic complete remission rate, R0 resection rate, and positive resection margin rate.ResultsA total of 17 studies with 18 173 patients were included. Among them, 13 were original studies with 2 950 patients, and 4 were database-based studies with a total of 15 223 patients. The results showed a significant positive correlation between the interval and operative mortality (Spearman coefficient=0.360, P=0.027). Dose-response meta-analysis revealed that there was a relatively better time window for surgery after nCRT. Further analysis for primary outcomes at different time cut-offs found the following results: (1) when the time cut-off point within 30-70 days, the shorter interval was associated with a reduced operative mortality (7-8 weeks: RR=0.67, 95% CI 0.55-0.81, P<0.05; 30-46 days: RR=0.63, 95%CI 0.47-0.85, P<0.05; 60-70 days: RR=0.64, 95%CI 0.48-0.85, P<0.05); (2) when the time cut-off point within 30-46 days, the shorter interval correlated with a reduced incidence of anastomotic leakage (RR=0.39, 95%CI 0.21-0.72, P<0.05); when the time cut-off point within 7-8 weeks, the shorter interval could achieve a critical-level effect of reducing the incidence of anastomotic leakage (RR=0.73, 95%CI 0.52-1.03, P>0.05); (3) when the time cut-off point within 7-8 weeks, increased interval significantly was associated with the poor overall survival (HR=1.17, 95% CI 1.00-1.36, P<0.05). Secondary outcomes found that the shorter interval could significantly reduce the positive resection margin rate (RR=0.53, 95% CI 0.38-0.75, P<0.05) when time cut-off point within 56-60 days.ConclusionShortening the interval between nCRT and surgery can reduce the operative mortality, the incidence of anastomotic leakage, long-term mortality risk, and positive resection margin rate. It is recommended that surgery should be performed as soon as possible after the patient's physical recovery, preferably no more than 7-8 weeks, which supports the current study recommendation (within 3-8 weeks after nCRT).
