Survivin as a potential biomarker in the diagnosis of bladder cancer: A systematic review and meta-analysis.

Early detection, diagnosis, and treatment take on critical significance in preventing and treating bladder cancer. As indicated by numerous studies, survivin can serve as a biomarker of bladder cancer, whereas the results of a wide variety of studies have been controversial. This paper is to assess the accuracy of survivin in the diagnosis of bladder cancer by a meta-analysis. The studies regarding the diagnosis of bladder cancer using survivin were systematically retrieved from the CNKI, WanFang, CBM, VIP, Web of science, cochrane library and pubmed were extracted, and the literature quality was assessed. Meta-analysis was conducted using STATA 16.0 MP. 2,082 relevant studies were searched, and 40 studies were finally covered for meta-analysis. The pooled specificity and pooled sensitivity of survivin mRNA was 0.95 (95%CI: 0.91, 0.97) and 0.94 (95%CI: 0.88, 0.97). The pooled specificity and pooled sensitivity of survivin protein reached 0.95 (95%CI: 0.90, 0.97) and 0.87 (95%CI: 0.78, 0.92). The pooled positive likelihood ratio, pooled negative likelihood ratio, the area under the curve, and diagnostic odds ratio for survivin mRNA reached 17.7 (95%CI: 10.3, 30.6), 0.07 (95%CI: 0.04, 0.12), 0.98 (95%CI: 0.97, 0.99) and 266 (95%CI: 114, 621), respectively. For survivin protein was 16.4 (95%CI: 7.9, 33.9), 0.14 (95%CI: 0.08, 0.24), 0.97 (95%CI: 0.95, 0.98) and 117 (95%CI: 38, 357), respectively. Survivin takes on great significance in diagnosing bladder cancer. However, due to some limitations in the number and quality of covered studies, this conclusion should be validated through additional higher quality clinical studies.

Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning.

Accurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall thermal load of a box structure. To determine the location and magnitude of the thermal load accurately, the proposed method segments a structure into several subregions and applies a cascade of deep learning models to gradually reduce the solution domain. The generalization ability of the model is significantly enhanced by the inclusion of boundary conditions in the deep learning models. In this study, a large simulated dataset was generated by varying the load application position and intensity for each sample. The input variables encompass a small set of structural displacements, while the outputs include parameters related to the thermal load, such as the position and magnitude of the load. Ablation experiments are conducted to validate the effectiveness of this approach. The results show that this method reduces the identification error of the thermal load parameters by more than 45% compared with a single deep learning network. The proposed method holds promise for optimizing the design and analysis of spacecraft structures, contributing to improved performance and reliability in future space missions.

[Genetic polymorphisms of 30 InDel loci in Chinese ethnic population residing in Tibet].

OBJECTIVE: To analyze the genetic data of 30 insertion and deletion polymorphisms (InDel) loci included in an InvestigatorR DIPplex diagnostic kit, and to evaluate the forensic application in ethnic Tibetan population from China. METHODS: By detecting 226 unrelated individuals with the Investigator(R) DIPplex kit, allelic frequencies and population genetics parameters of the 30 InDels were statistically analyzed and compared with available data derived from other populations from various regions. RESULTS: After the Bonferroni correction at a 95% significance level (P=0.0017), no significant departures from the Hardy-Weinberg equilibrium were observed except for the HLD114 locus. Linkage disequilibrium test showed no significant allelic association between all 30 loci after the Bonferroni's correction. The average heterozygosity (Ho) of all loci was 0.4125, the mean discrimination power (DP) was 0.5618, the mean polymorphism information content (PIC) was 0.3280, and the combined discrimination power (TDP) was 0.999999999990. The combined power of exclusion of all loci was 0.987 849 91 in trio cases and 0.94977125 in duo cases. Genetic distance between Tibetan and Han from Beijing was minimum (0.0068) in the 5 populations, while genetic distance between Tibetan and Uygur was maximal (0.0215). CONCLUSION: Multiplex detection has revealed that these 30 InDel loci have a moderate distribution of genetic polymorphism among ethnic Tibetan group residing in Tibet, China.

Population data of 30 insertion-deletion markers in four Chinese populations.

In this study, we assessed 30 insertion-deletion polymorphisms (Indels) (Investigator DIPplex® kit) in four Chinese populations (n = 952) and evaluated their usefulness in forensic genetic applications. After the Bonferroni correction at a 95 % significance level (p = 0.0017), there were no deviations from the Hardy-Weinberg equilibrium observed except for the HLD114 locus in the Tibetan ethnic group studied. A high level of discrimination power was observed for the DIPplex® kit in four sample populations (CDP > 0.9999) and the combined random match probabilities (CMP) were in the range of 1.4766 × 10(-11) to 5.2742 × 10(-13). Four Indels have been selected for further analyses as possible ancestry informative single nucleotide polymorphisms. The study support that the Investigator DIPplex® kit provides a powerful supplement to standard short tandem repeat-based kits for individual identification and kinship analysis in the Chinese population.

[Genetic polymorphism and forensic application of 30 InDel loci of Han population in Beijing].

To study the genetic diversities of 30 insertion-deletion (InDel) polymorphisms loci of Han population in Beijing, and to evaluate their forensic application, 210 unrelated healthy individuals of Han population in Beijing were investigated to determine the distributions of allele frequencies by using Investigator DIP system. The PCR products were detected with ABI 3130 XL Genetic Analyzer. Forensic parameters were calculated with relevant statistical analysis software. As a result, after the Bonferroni correction at a 95% significance level, there were no significant departures from Hardy-Weinberg equilibrium or significant linkage disequilibrium between the loci. The power of discrimination (DP) varies between 0.2690 (HLD118) and 0.6330 (HLD45), and the combined discrimination power (TDP) for the 30 InDel loci is 0.999999999985. The combined power of exclusion was 0.98771049 in trio cases (CPE(trio)) and 0.94579456 in duo cases (CPE(duo)). The parentage testing of 32 cases revealed no mutations happened to 30 InDel loci. Multiplex detection of the 30 InDel loci revealed a highly polymorphic genetic distribution in Beijing Han population, which represents a complementary tool in human identification studies, especially in challenging DNA cases.