Intraoperative Intraocular Lens Waste: Incidence, Cost and Reasons.

PURPOSE: To evaluate the incidence and cost of intraocular lens(IOL) waste during IOL implantation, as well as the reasons for it. METHODS: A retrospective analysis was conducted on the data of 485 patients from the IOL waste registers of a single tertiary eye hospital in China during 2016-2020. The primary outcomes were the incidence, cost, and reasons for different IOL properties. Cases were examined to ascertain IOL material, design, procedural details, and causes of waste. RESULTS: IOL waste occurred in 485 (6.62‰) of the 73,246 IOL implantations during the study period. The total cost of IOL waste was 429, 850.26 Chinese Yuan (CNY) related to waste with an average cost of 2, 442.33 CNY per procedure during the study period. Comparisons between IOL properties showed that polymethyl methacrylate (PMMA) material (39, 2.05%), three-piece design (142, 1.49%), and secondary IOL implantation (26, 2.16%) were associated with IOL wastage, and the difference was statistically significant. The causes of IOL waste were damage (107, 60.80%), patient reasons (37, 21.26%), aseptic errors (22, 12.50%), IOL quality problems (8, 4.55%), and loss (2, 1.14%). CONCLUSIONS: The incidence of IOL waste is low, but still leads to a significant cost burden due to a large number of cataract surgeries. PMMA material, three-piece design, and secondary implantation were identified as factors increasing IOL waste. Damage emerged as the primary reason for waste, largely attributed to human error. Therefore, the development of strategies to mitigate IOL waste is imperative.

Prognostic Implications of RAB35 and its Relationship With Immune Microenvironment in Pan-cancer.

Context: Ras-associated binding 35 (RAB35) is an oncogenic, guanosine triphosphate (GTP)ase that plays a role in cancer invasion, metastasis, and immune evasion. However, systematic and comprehensive research to identify the importance of RAB35 in various cancer types is still absent. Objective: The study intended to explore the potential value of RAB35 as a molecular biomarker. Design: The research team performed a genetic evaluation of RAB35. Setting: The study took place at the Second Affiliated Hospital of Wenzhou Medical University in Wenzhou, China. Outcome Measures: The research team assessed the expression of RAB35 in various tumor tissues and performed correlation analyses between RAB35 expression and prognosis, molecular subtypes, immunological subtypes, immune-associated cell infiltration, the tumor immune microenvironment, and drug sensitivity in pan-cancer. Results: RAB35 exhibited significant differential expression for 21 cancer types. It demonstrated high sensitivity and specificity in diagnosing eight cancer types, showed distinct expression patterns in various molecular subtypes for six cancer types, and found different immune subtypes for eight cancer types. The abnormal expression of RAB35 was significantly related to overall survival (OS) for nine cancer types, progress free interval (PFI) for five cancer types, and disease-specific survival (DSS) for five cancer types. Its abnormal expression was closely associated with the immune microenvironment and multiple immune cells. Furthermore, it was related to the drug sensitivity for various drugs and might be associated with chemotherapy resistance. Conclusions: RAB35 showed significant differential expression in various cancers and was significantly related to the prognosis of cancer patients, the immune microenvironment, multiple immune cells, drug sensitivity to various drugs, and chemotherapy resistance. It may serve as a potential biomarker and therapeutic target for cancer treatment.

Two-phase SSU and SKAT in genetic association studies.

The sum of squared score (SSU) and sequence kernel association test (SKAT) are the two good alternative tests for genetic association studies in case-control data. Both SSU and SKAT are derived through assuming a dose-response model between the risk of disease and genotypes. However, in practice, the real genetic mode of inheritance is impossible to know. Thus, these two tests might losepower substantially as shown in simulation results when the genetic model is misspecified. Here, to make both the tests suitable in broad situations, we propose two-phase SSU (tpSSU) and two-phase SKAT (tpSKAT), where the Hardy-Weinberg equilibrium test is adopted to choose the genetic model in the first phase and the SSU and SKAT are constructed corresponding to the selected genetic model in the second phase. We found that both tpSSU and tpSKAT outperformed the original SSU and SKAT in most of our simulation scenarios. Byapplying tpSSU and tpSKAT to the study of type 2 diabetes data, we successfully identified some genes that have direct effects on obesity. Besides, we also detected the significant chromosomal region 10q21.22 in GAW16 rheumatoid arthritis dataset, with P<10-6. These findings suggest that tpSSU and tpSKAT can be effective in identifying genetic variants for complex diseases in case-control association studies.

A Powerful Method To Test Associations Between Ordinal Traits and Genotypes.

The methods commonly used to test the associations between ordinal phenotypes and genotypes often treat either the ordinal phenotype or the genotype as continuous variables. To address limitations of these approaches, we propose a model where both the ordinal phenotype and the genotype are viewed as manifestations of an underlying multivariate normal random variable. The proposed method allows modeling the ordinal phenotype, the genotype and covariates jointly. We employ the generalized estimating equation technique and M-estimation theory to estimate the model parameters and deduce the corresponding asymptotic distribution. Numerical simulations and real data applications are also conducted to compare the performance of the proposed method with those of methods based on the logit and probit models. Even though there may be potential limitations in Type I error rate control for our method, the gains in power can prove its practical value in case of exactly ordinal phenotypes.

A statistical measure for the skewness of X chromosome inactivation based on case-control design.

BACKGROUND: Skewed X chromosome inactivation (XCI), which is a non-random process, is frequently observed in both healthy and affected females. Furthermore, skewed XCI has been reported to be related to many X-linked diseases. However, no statistical method is available in the literature to measure the degree of the skewness of XCI for case-control design. Therefore, it is necessary to develop methods for such a task. RESULTS: In this article, we first proposed a statistical measure for the degree of XCI skewing by using a case-control design, which is a ratio of two logistic regression coefficients after a simple reparameterization. Based on the point estimate of the ratio, we further developed three types of confidence intervals (the likelihood ratio, Fieller's and delta methods) to evaluate its variation. Simulation results demonstrated that the likelihood ratio method and the Fieller's method have more accurate coverage probability and more balanced tail errors than the delta method. We also applied these proposed methods to analyze the Graves' disease data for their practical use and found that rs3827440 probably undergoes a skewed XCI pattern with 68.7% of cells in heterozygous females having the risk allele T active, while the other 31.3% of cells keeping the normal allele C active. CONCLUSIONS: For practical application, we suggest using the Fieller's method in large samples due to the non-iterative computation procedure and using the LR method otherwise for its robustness despite its slightly heavy computational burden.

Efficient estimation of disease odds ratios for follow-up genetic association studies.

In the past decade, genome-wide association studies have identified thousands of susceptible variants associated with complex human diseases and traits. Conducting follow-up genetic association studies has become a standard approach to validate the findings of genome-wide association studies. One problem of high interest in genetic association studies is to accurately estimate the strength of the association, which is often quantified by odds ratios in case-control studies. However, estimating the association directly by follow-up studies is inefficient since this approach ignores information from the genome-wide association studies. In this article, an estimator called GFcom, which integrates information from genome-wide association studies and follow-up studies, is proposed. The estimator includes both the point estimate and corresponding confidence interval. GFcom is more efficient than competing estimators regarding MSE and the length of confidence intervals. The superiority of GFcom is particularly evident when the genome-wide association study suffers from severe selection bias. Comprehensive simulation studies and applications to three real follow-up studies demonstrate the performance of the proposed estimator. An R package, "GFcom", implementing our method is publicly available at https://github.com/JiyuanHu/GFcom .

Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies.

Principal component analysis (PCA) is a useful tool to identify important linear combination of correlated variables in multivariate analysis and has been applied to detect association between genetic variants and human complex diseases of interest. How to choose adequate number of principal components (PCs) to represent the original system in an optimal way is a key issue for PCA. Note that the traditional PCA, only using a few top PCs while discarding the other PCs, might significantly lose power in genetic association studies if all the PCs contain non-ignorable signals. In order to make full use of information from all PCs, Aschard and his colleagues have proposed a multi-step combined PCs method (named mCPC) recently, which performs well especially when several traits are highly correlated. However, the power superiority of mCPC has just been illustrated by simulation, while the theoretical power performance of mCPC has not been studied yet. In this work, we attempt to investigate theoretical properties of mCPC and further propose a novel and efficient strategy to combine PCs. Extensive simulation results confirm that the proposed method is more robust than existing procedures. A real data application to detect the association between gene TRAF1-C5 and rheumatoid arthritis further shows good performance of the proposed procedure.

The Potential Influence of Bumble Bee Visitation on Foraging Behaviors and Assemblages of Honey Bees on Squash Flowers in Highland Agricultural Ecosystems.

Bee species interactions can benefit plant pollination through synergistic effects and complementary effects, or can be of detriment to plant pollination through competition effects by reducing visitation by effective pollinators. Since specific bee interactions influence the foraging performance of bees on flowers, they also act as drivers to regulate the assemblage of flower visitors. We selected squash (Cucurbita pepo L.) and its pollinators as a model system to study the foraging response of honey bees to the occurrence of bumble bees at two types of sites surrounded by a high amount of natural habitats (≥ 58% of land cover) and a low amount of natural habitats (≤ 12% of land cover) in a highland agricultural ecosystem in China. At the individual level, we measured the elapsed time from the departure of prior pollinator(s) to the arrival of another pollinator, the selection of honey bees for flowers occupied by bumble bees, and the length of time used by honey bees to explore floral resources at the two types of sites. At the community level, we explored the effect of bumble bee visitation on the distribution patterns of honey bees on squash flowers. Conclusively, bumble bee visitation caused an increase in elapsed time before flowers were visited again by a honey bee, a behavioral avoidance by a newly-arriving honey bee to select flowers occupied by bumble bees, and a shortened length of time the honey bee takes to examine and collect floral resources. The number of overall bumble bees on squash flowers was the most important factor explaining the difference in the distribution patterns of honey bees at the community level. Furthermore, decline in the number of overall bumble bees on the squash flowers resulted in an increase in the number of overall honey bees. Therefore, our study suggests that bee interactions provide an opportunity to enhance the resilience of ecosystem pollination services against the decline in pollinator diversity.

The complete mitochondrial genome of Cuora flavomarginata (Testudines: Testudinoidea): genome description and related phylogenetic analyses.

Cuora flavomarginata (Chinese box turtle), a semi aquatic turtle, was listed as a Vulnerable species in the China Species Red List and Convention on International Trade in Endangered Species. In the present study, we reported the complete mitochondrial genome of C. flavomarginata. The genome of C. flavomarginata was 16,721 bp in length, containing 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes, a control region (D-loop), and an L-strand replication origin (OL). Besides, we conducted the comparison with two different individuals of C. flavomarginata and phylogenetic analysis with closely related species. This work contributed to providing basic molecular data for the conservation of this endangered species.

The complete mitochondrial genome of Mongolian redfin, Chanodichthys mongolicus (Cypriniformes: Cyprinidae): genome description and related phylogenetic analyses.

Chanodichthys mongolicus is a ferocious carnivorous species in the family Cyprinidae, which has a high economic value in freshwater aquaculture species. In the present study, we report the complete mitochondrial genome of C. mongolicus, comparing with two different individuals of C. mongolicus and phylogenetic analysis with closely related species. The complete mitochondrial genome of C. mongolicus was 16,622 bp, and it contained 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes, a control region (D-loop) and an L-strand replication origin (OL). It revealed that D-loop, ND4, ND5 and CYTB may be markers for studying conservation genetics and population genetics.

The complete mitochondrial genome of Saurogobio dumerili (Cypriniformes: Cyprinidae).

Saurogobio dumerili is a cyprinid fish endemic to China. In this study, the complete mitochondrial genome of S. dumerili is reported, a circular molecule of 16,601 bp in length, with a base composition of 29.75% A, 25.71% T, 16.80% G and 27.75% C. It consists of 13 protein-coding genes, 2 rRNAs, 22 tRNAs, 1 control region and a replication origin region (OL), with the typical pattern for vertebrates. The origin of L-strand replication (OL) in S. dumerili was located in a cluster of five tRNA genes (WANCY region) with 33 bp in length.

The complete mitochondrial genome of Pseudobagrus trilineatus (Siluriformes: Bagridae).

Pseudobagrus trilineatus was listed as a vulnerable species in the China Species Red List. In the present paper, we reported the complete mitochondrial genome of P. trilineatus. The genome was 16,535 bp in length, containing 13 protein-coding genes, two rRNA genes, 22 tRNA genes and a control region, a gene organization similar to that of typical vertebrate mitochondrial genome. The overall nucleotide base composition of P. trilineatus mitogenome was A (31.56%), T (26.48%), C (27.13%) and G (14.83%), with an AT content of 58.04%. This study contributes to providing new molecular data for the conservation of this endangered species.

The complete mitochondrial genome of Chinese lizard gudgeon, Saurogobio dabryi (Cypriniformes: Cyprinidae).

Saurogobio dabryi is a small-sized benthopelagic fish species in the famally Cyprinidae. In the present study, the complete mitochondrial genome of S. dabryi was determined. The complete mitochondrial genome of S. dabryi was 16,601 bp in length with the overall nucleotide base composition of A (29.66%), T (26.50%), G (16.80%) and C (27.04%), as well as an AT content of 56.16%. It contained 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes, a control region and an L-strand replication origin (OL).

Detection of parent-of-origin effects for quantitative traits using general pedigree data.

Genomic imprinting is a genetic phenomenon in which certain alleles are differentially expressed in a parent-of-origin-specific manner, and plays an important role in the study of complex traits. For a diallelic marker locus in human, the parentalasymmetry tests Q-PAT(c) with any constant c were developed to detect parent-of-origin effects for quantitative traits. However, these methods can only be applied to deal with nuclear families and thus are not suitable for extended pedigrees. In this study, by making no assumption about the distribution of the quantitative trait, we first propose the pedigree parentalasymmetry tests Q-PPAT(c) with any constant c for quantitative traits to test for parent-of-origin effects based on nuclear families with complete information from general pedigree data, in the presence of association between marker alleles under study and quantitative traits. When there are any genotypes missing in pedigrees, we utilize Monte Carlo (MC) sampling and estimation and develop the Q-MCPPAT(c) statistics to test for parent-of-origin effects. Various simulation studies are conducted to assess the performance of the proposed methods, for different sample sizes, genotype missing rates, degrees of imprinting effects and population models. Simulation results show that the proposed methods control the size well under the null hypothesis of no parent-of-origin effects and Q-PPAT(c) are robust to population stratification. In addition, the power comparison demonstrates that Q-PPAT(c) and Q-MCPPAT(c) for pedigree data are much more powerful than Q-PAT(c) only using two-generation nuclear families selected from extended pedigrees.

Robust joint analysis with data fusion in two-stage quantitative trait genome-wide association studies.

Genome-wide association studies (GWASs) in identifying the disease-associated genetic variants have been proved to be a great pioneering work. Two-stage design and analysis are often adopted in GWASs. Considering the genetic model uncertainty, many robust procedures have been proposed and applied in GWASs. However, the existing approaches mostly focused on binary traits, and few work has been done on continuous (quantitative) traits, since the statistical significance of these robust tests is difficult to calculate. In this paper, we develop a powerful F-statistic-based robust joint analysis method for quantitative traits using the combined raw data from both stages in the framework of two-staged GWASs. Explicit expressions are obtained to calculate the statistical significance and power. We show using simulations that the proposed method is substantially more robust than the F-test based on the additive model when the underlying genetic model is unknown. An example for rheumatic arthritis (RA) is used for illustration.

Evaluating rare variants under two-stage design.

Current genome-wide association studies (GWAS) focusing on relatively common single-nucleotide polymorphisms (SNPs) usually adopt a cost-effective multi-staged design in which a proportion of the total samples are genotyped using a commercial SNP array with a reasonably good coverage of the whole genome at the initial stage, and a list of promising SNPs are further genotyped and evaluated on the remaining samples at the second stage. This staged design in principal can also be used for the study of rare genetic variants at the genome-wide scale, but the statistical methods developed for evaluating the relatively common SNPs under the staged design are not appropriate for rare variants due to the invalidity of large sample theorems. Here, we develop a new statistical framework that aims to evaluate rare variants under two-staged (or multi-staged) design. By extensive computer simulations, we evaluate the empirical type I error rate and power of the proposed procedures. A real example from two recent case-control rheumatoid arthritis genetic association studies is also used to demonstrate the performances of the proposed methods.

Robust joint analysis allowing for model uncertainty in two-stage genetic association studies.

BACKGROUND: The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate. RESULTS: In this paper, we propose joint analyses based on two robust tests, MERT and MAX3, for GWASs under a two-stage design. We developed computationally efficient procedures and formulas for significant level evaluation and power calculation. The performances of the proposed approaches are investigated through the extensive simulation studies and a real example. Numerical results show that the joint analysis based on the MAX3 test statistic has the best overall performance. CONCLUSIONS: MAX3 joint analysis is the most robust procedure among the considered joint analyses, and we recommend using it in a two-stage genome-wide association study.