Objective To investigate the pathological mechanism of epileptic comorbid sleep disorder by analyzing the changes of cerebral white matter diffusion tensor in patients with sleep disorder with negative magnetic resonance imaging (MRI) epilepsy based on the method of tract-based spatial statistics (TBSS). Methods MRI negative epilepsy patients comorbid sleep disorder who were epileptic patients treated l in China-Japan Union Hospital of Jilin University from January 2020 to December 2022 completed the Epworth sleepiness scale (ESS) and Pittsburgh sleep quality index (PSQI) tests, and those who complained of sleep disorder and PSQI index ≥11 were monitored by nighttime polysomnography (PSG) and those with objective sleep disorder confirmed by PSG were included in the epilepsy comorbid sleep disorder group. Healthy volunteers with matching gender, age, education were included in the health control group. Diffusion tensor image ( DTI) was collected for all subjects by using a 3.0T magnetic resonance scanner. Diffusion parameters were compared between the two groups using TBSS. Results This study included 36 epilepsy patients comorbid sleep disorder and 35 healthy volunteers. epilepsy patients comorbid sleep disorder showed significantly lower fraction anisotropy (FA) (P<0.05) and significantly higher mean diffusivity (MD) (P<0.05) than the health control group . Brain regions with statistical differences in FA reduction included middle peduncle of cerebellum, genu of corpus callosum, body of corpus callosum, splenium of corpus callosum, anterior corona radiata, external capsule and right posterior thalamic radiation.Brain regions with statistical differences in MD degradation included genu of corpus callosum, body of corpus callosum, anterior limb of internal capsule, anterior corona radiata, superior corona radiata, external capsule and right posterior limb of internal capsul. Conclusion Patients with epilepsy comorbidities with sleep disorders have widespread and symmetric white matter damage.The white matter damage is concentrated in the front of the brain.
Objective To investigate confidence interval estimation for the amount of heterogeneity in meta-analysis. Methods On the basis of BT’s method, the approximate Q-statistic distribution following linear transformation of Chi-square was applied to improve the accuracy of Q-statistic distribution, and to obtain the confidence interval for the amount of heterogeneity in meta-analysis. Results In case, the Q1 distribution obtained 95%CI 0.07 to 2.20, while the Q2 distribution obtained 95%CI 0.00 to 1.41; The proposed method Q2 narrowed down the range of confidence interval. Conclusion On account of improving the accuracy of Q-statistic distribution, the proposed method effectively strengthens the coverage probabilities of the confidence interval for the amount of heterogeneity. And the proposed method can also improve the precision of the confidence interval estimation for the amount of heterogeneity.
In 2025, the American Cancer Society published "Cancer statistics, 2025", which projected cancer data for the upcoming year based on incidence data collected by central cancer registries (through 2021) and mortality data obtained from the National Center for Health Statistics (through 2022). Similarly, the National Cancer Center of China released "Cancer incidence and mortality in China, 2022" in December 2024, analyzing data from 22 cancer registries across the country. This study provides a comparative analysis of cancer incidence and mortality trends in China and the United States during the same period, with a focus on sex- and age-specific distributions and long-term changes in cancer patterns. Long-term trends indicate that lung and liver cancer mortality rates in China have declined, primarily due to tobacco control measures and hepatitis B vaccination programs. However, the burden of gastric and esophageal cancers remains substantial. In the United States, mortality rates for colorectal and lung cancers have continued to decline, largely attributed to widespread screening programs and advances in immunotherapy. As economic growth and social development, China’s cancer profile is gradually shifting towards patterns observed in countries with high human development index. However, the prevention and control of upper gastrointestinal cancers remains a critical public health challenge that requires further attention.
For speech detection in Parkinson’s patients, we proposed a method based on time-frequency domain gradient statistics to analyze speech disorders of Parkinson’s patients. In this method, speech signal was first converted to time-frequency domain (time-frequency representation). In the process, the speech signal was divided into frames. Through calculation, each frame was Fourier transformed to obtain the energy spectrum, which was mapped to the image space for visualization. Secondly, deviations values of each energy data on time axis and frequency axis was counted. According to deviations values, the gradient statistical features were used to show the abrupt changes of energy value in different time-domains and frequency-domains. Finally, KNN classifier was applied to classify the extracted gradient statistical features. In this paper, experiments on different speech datasets of Parkinson’s patients showed that the gradient statistical features extracted in this paper had stronger clustering in classification. Compared with the classification results based on traditional features and deep learning features, the gradient statistical features extracted in this paper were better in classification accuracy, specificity and sensitivity. The experimental results show that the gradient statistical features proposed in this paper are feasible in speech classification diagnosis of Parkinson’s patients.
Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.
ObjectiveTo analyze the risk factors of severe retinopathy of prematurity (ROP) and provide consultable evidence for the rational establishment of screening standard.MethodsThe clinical data of 168 prematureinfants (gestational age less than 37 weeks) who was diagnosed in our department from Dec 2002 to Apr 2004 was analyzed retrospectively. Gender, birth count (BC), gestational age (GA), birth weight (BW), duration of oxygen therapy and vascularization devlopment of posterior and peripheral retina examined by binocular indirect ophthalmoscope after mydriasis were recorded. The results were recorded by the international classification of ROP (ICROP), and stage 1, 2 and 3 were mild ROP while threshold disease, stage 4 and 5 were severe ROP. Logistic regression was appliedto analyze the relationship of ROP and gender, BC, GA, BW, and oxygen therapy. ResultsSevere ROP was found in 91 eyes (27.1%) of 47 infants (28.0%) in 336 eyes of 168 premature infants, including threshold disease in 20 eyes (6.0%) and disease at stage 4 in 11 eyes (3.3%) in which the diseases at stage 4A was foundin 2 eyes (0.6%) and stage 4B in 9 eyes (2.7%). There were 60 eyes (17.8%) at stage 5. In all of the factors, GA, BW and oxygen therapy were found to have a significant impact on severe ROP (P=0.000, 0.000 and 0.015,α=0.05) while gender and BC were not (P=0.640 and 0.084, α=0.05). Statistic analysis of subgroupshowed that the risk of severe ROP in premature infants would increase significantly when GA≤30 weeks, BW≤1500 g or oxygen therapy gt;4 days. Conclusions Severe ROP relates to GA, BW and oxygen therapy instead of gender and BC. The risk of occurrence of severe ROP in premature infants increases significantly when GA≤30 weeks, BW≤1500g or oxygen therapy gt;4 days, so it is recommended to screen such premature infants carefully. (Chin J Ocul Fundus Dis,2005,21:271-274)
The selection of summary statistics to use in a meta-analysis is very important for the interpretation and application of its results. This paper introduces some basic concepts of summary statistics in meta-analysis. The selection of a summary statistic for a meta-analysis depends on the following factors: design of the studies being combined, type of data, consistency among the included studies, mathematical properties and ease of interpretation. For continuous data, the weighted mean difference (WMD) is recommended when all trials use the same scale to report their outcomes, while standardized mean difference (SMD) is more appropriate when trials use different scales to report their outcomes, or the means of their outcomes differ greatly. For dichotomous data, rate ratio or relative risk (RR) is bly recommended to be the summary statistics for meta-analyses of randomized trials. The use of odds ratio (OR) as the summary statistic is similar to that of RR, if the event being studied in both the intervention (exposure) and the control group is rare. There is no single measurement that is uniformly best for all meta-analyses.
This study aims to determine the salient brain regions with abnormal changes in white matter structures from diffusion tensor imaging (DTI) images of the patients with temporal lobe epilepsy (TLE), and to discriminate the patients with TLE from normal controls (NCs). Firstly, the DTI images from 50 subjects (28 NCs and 22 TLE) were acquired. Secondly, the four measures including the fractional anisotropy (FA), the mean diffusivity (MD), the axial diffusivity (AD) and the radial diffusivity (RD) were calculated. Thirdly, the tract-based spatial statistics (TBSS) was adopted to extract the measures in brain regions with significant differences between the two compared groups. Fourthly, the obtained measures were used as input features of the support vector machine (SVM) for classification, and the support vector machine-recursive feature elimination (SVM-RFE) was compared with the support vector machine-tract-based spatial statistics (SVM-TBSS) method. Finally, the essential brain regions and their spatial distribution were analyzed and discussed. The experimental results showed that the FA measures of the TLE group decreased significantly in the corpus callosum, superior longitudinal fasciculus, corona radiata, external capsule, internal capsule, inferior fronto-occipital fasciculus, fasciculus uncinatus and sagittal stratum, which were nearly bilaterally distributed, while the MD and RD increased significantly in most of these brain regions of the TLE group. Although the AD also increased, the differences were not statistically significant. The SVM-TBSS classifier obtained accuracies of 82%, 76% and 76% using the FA, MD and RD for classification, respectively, and 80% using combined measures. The SVM-RFE classifier obtained accuracies of 90%, 90% and 92% using the FA, MD and RD respectively, while the highest accuracy was 100% using combined measures. These results demonstrated that the SVM-RFE outperformed the SVM-TBSS, and the dominant characteristic influencing classification in brain regions were in associative and commissural fibers. These results illustrated that the measures of DTI images could reveal the abnormal changes in white matter structure of patients with TLE, providing effective information to clarify its pathological mechanism, localize the focus and diagnose automatically.
The WinBUGS software can be called from either R (provided R2WinBUGS as an R package) or Stata software for network meta-analysis. Unlike R, Stata software needs to create relevant ADO scripts at first which simplify operation process greatly. Similar with R, Stata software also needs to load another package when drawing network plots. This article briefly introduces how to implement network meta-analysis using Stata software by calling WinBUGS software.
The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.