Tag: statistics

J Neurooncol. 2022 May 31. doi: 10.1007/s11060-022-04038-z. Online ahead of print.

ABSTRACT

INTRODUCTION: The treatment of brain metastases with stereotactic radiosurgery (SRS) in combination with immune checkpoint inhibitors (ICI) has become more common in recent years, but there is a lack of prospective data on cancer control outcomes when these therapies are administered concurrently.

METHODS: Data were retrospectively reviewed for patients with non-small cell lung cancer (NSCLC) and melanoma brain metastases treated with SRS at a single institution from May 2008 to January 2017. A parametric proportional hazard model is used to detect the effect of concurrent ICI within 30, 60, or 90 days of ICI administration on local control and distant in-brain control. Other patient and lesion characteristics are treated as covariates and adjusted in the regression. A frailty term is added in the baseline hazard to capture the within-patient correlation.

RESULTS: We identified 144 patients with 477 total lesions, including 95 NSCLC patients (66.0%), and 49 (34.0%) melanoma patients. On multivariate analysis, concurrent SRS and ICI (SRS within 30 days of ICI administration) was not associated with local control but was associated with distant brain control. When controlling for prior treatment to lesion, number of lesions, and presence of extracranial metastases, patients receiving this combination had a statistically significant decrease in distant brain failure compared to patients that received non-concurrent ICI or no ICI (HR 0.15; 95% CI 0.05-0.47, p = 0.0011).

CONCLUSION: Concurrent ICI can enhance the efficacy of SRS. Prospective studies would allow for stronger evidence to support the impact of concurrent SRS and ICI on disease outcomes.

PMID:35641840 | DOI:10.1007/s11060-022-04038-z

Clin Oral Investig. 2022 May 31. doi: 10.1007/s00784-022-04567-x. Online ahead of print.

ABSTRACT

OBJECTIVES: To compare the center of resistance (Cres) of the maxillary central incisor in models with and without the pulp cavity and to evaluate the association of pulp cavity/tooth volume ratio and difference in Cres position between the two models.

MATERIALS AND METHODS: CBCT images of the right maxillary central incisor were collected from 18 subjects. Pulp cavity/tooth volume ratio was measured, and finite element models of teeth and periodontal structures were generated. Cres location was presented as a percentage of root length measured from the root apex. Differences in Cres positions between models were compared using the paired t-test, while the correlation between pulp cavity/tooth volume ratio and a difference in Cres was evaluated by Pearson’s correlation coefficient.

RESULTS: For the pulp cavity model, the average location of the Cres measured from the apex of the root was 58.8% ± 3.0%, which resulted in a difference of 4.1% ± 1.1% (0.5 mm) apically, when compared with the model without pulp cavity. Differences in Cres between the models were statistically significant (P < 0.01), while the correlation between pulp cavity/tooth volume ratio and a difference in Cres between models was significantly positive (r = 0.709, P = 0.001).

CONCLUSIONS: In the pulp cavity model, the Cres was located in a more apical position. The difference in Cres between models increased as the pulp cavity/tooth volume ratio increased.

CLINICAL RELEVANCE: The line of force must be applied more apically in the pulp cavity model to achieve the desired orthodontic tooth movement.

PMID:35641835 | DOI:10.1007/s00784-022-04567-x

Nat Med. 2022 May 31. doi: 10.1038/s41591-022-01842-y. Online ahead of print.

NO ABSTRACT

PMID:35641824 | DOI:10.1038/s41591-022-01842-y

J Gastrointest Surg. 2022 May 31. doi: 10.1007/s11605-022-05368-z. Online ahead of print.

ABSTRACT

BACKGROUND: Sequential extended cholecystectomy (SEC) is currently recommended for T2 and higher gallbladder cancer (GBC) diagnosed after simple cholecystectomy (SC), but the value and timing of re-resection has not been fully studied. We evaluated the long-term oncologic outcomes of T2 GBC according to the type of surgery performed and investigated the optimal timing for SEC.

METHODS: Patients diagnosed with T2 GBC who underwent SC, extended cholecystectomy (EC), or SEC between 2002 and 2017 were retrospectively reviewed. Those who underwent other surgical procedures or those with incomplete medical records were excluded. Overall survival (OS) and disease-free survival (DFS) according to the types of surgeries and prognostic factors for OS and DFS were analyzed. Survival analysis was done between groups that were divided according to the optimal cutoff time interval between SC and SEC based on DFS data.

RESULTS: Of the 226 T2 GBC patients, 53, 173, and 44 underwent SC, EC, and SEC, respectively. The 5-year OS rate was 50.1%, 73.2%, and 78.7%, and the DFS rate was 46.8%, 66.3%, and 65.2% in the SC, EC, and SEC groups, respectively. EC (p = 0.001 and p = 0.001) and SEC (p = 0.007 and p = 0.065) groups had better 5-year OS and DFS rates than the SC group. Preoperative CA 19-9 level > 37 U/mL (HR 1.56; 95% CI 1.87-2.79; p < 0.001) and N1 stage (HR 2.88; 95% CI 1.76-4.71; p < 0.001) were associated with poorer prognosis. The optimal cutoff interval between SC and SEC was 28 days. Patients who underwent SEC ≤ 28 days after the initial cholecystectomy had better 5-year DFS rates than patients who underwent SEC after > 28 days (75.0% vs. 52.8%, p = 0.023).

CONCLUSIONS: SEC is recommended for T2 GBC diagnosed after SC, because SEC provides better survival outcomes than SC alone. A time interval of less than 28 days to SEC is associated with an improved DFS.

PMID:35641810 | DOI:10.1007/s11605-022-05368-z

Neurocrit Care. 2022 Jun 1. doi: 10.1007/s12028-022-01529-9. Online ahead of print.

ABSTRACT

BACKGROUND: Cerebral ischemia due to hypoxia is a major cause of secondary brain injury and is associated with higher morbidity and mortality in patients with acute brain injury. Hyperoxia could improve energetic dysfunction in the brain in this setting. Our objectives were to perform a systematic review and meta-analysis of the current literature and to assess the impact of normobaric hyperoxia on brain metabolism by using cerebral microdialysis.

METHODS: We searched Medline and Scopus, following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement; we searched for retrospective and prospective observational studies, interventional studies, and randomized clinical trials that performed a hyperoxia challenge in patients with acute brain injury who were concomitantly monitored with cerebral microdialysis. This study was registered in PROSPERO (CRD420211295223).

RESULTS: We included a total of 17 studies, with a total of 311 patients. A statistically significant reduction in cerebral lactate values (pooled standardized mean difference [SMD] – 0.38 [- 0.53 to – 0.23]) and lactate to pyruvate ratio values (pooled SMD – 0.20 [- 0.35 to – 0.05]) was observed after hyperoxia. However, glucose levels (pooled SMD – 0.08 [- 0.23 to 0.08]) remained unchanged after hyperoxia.

CONCLUSIONS: Normobaric hyperoxia may improve cerebral metabolic disturbances in patients with acute brain injury. The clinical impact of such effects needs to be further elucidated.

PMID:35641804 | DOI:10.1007/s12028-022-01529-9

Int J Biometeorol. 2022 May 31. doi: 10.1007/s00484-022-02306-1. Online ahead of print.

ABSTRACT

Cashew is an important cash crop which is ecologically sensitive, making it vulnerable to climate change. So, the present study compares the performance of stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), elastic net, and artificial neural network (ANN) individually against the ANN model combined with SLR, LASSO, elastic net, and principal components analysis (PCA) for prediction of cashew yield based on weather parameters. The model performances were evaluated using three approaches: (1) Taylor plot; (2) statistical metrics like coefficient of determination (R²), root mean square error (RMSE), and normalized RMSE (nRMSE); and (3) ranking followed by Kruskal-Wallis and Dunn’s post hoc test. The results revealed that during calibration, the R² and RMSE ranged from 0.486 to 0.999 and 2.184 to 88.040 kg ha^-1, respectively, while RMSE and nRMSE varied from 3.561 to 242.704 kg ha^-1 and 0.799 to 89.949%, respectively, during validation. Kruskal-Wallis and Dunn’s post hoc test revealed LASSO as the best model which was at par with ELNET, SLR, and ELNET-ANN. So, these models can be used for cashew yield prediction for the study area well in advance.

PMID:35641796 | DOI:10.1007/s00484-022-02306-1

Nat Rev Neurosci. 2022 May 31. doi: 10.1038/s41583-022-00601-9. Online ahead of print.

ABSTRACT

Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models – from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks – that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.

PMID:35641793 | DOI:10.1038/s41583-022-00601-9

Methods Mol Biol. 2022;2481:353-367. doi: 10.1007/978-1-0716-2237-7_20.

ABSTRACT

Plants form intimate associations with microorganisms, and these associations are directly impacted by the host genotype. However, identifying specific host genetic pathways that influence these microbial interactions has proved challenging. Genome-wide association-based approaches that use features of microbiome composition as a quantitative trait represent a novel and underutilized strategy to identify such pathways. Several recent studies have demonstrated the potential utility of plant microbiome-based genome-wide association studies (GWAS). In this chapter, we describe the process of implementing GWAS using the plant microbiome as the primary quantitative trait, considering experimental design, sample harvest, and processing, but with an emphasis on data filtering, data normalization, and statistical analyses.

PMID:35641774 | DOI:10.1007/978-1-0716-2237-7_20

Methods Mol Biol. 2022;2481:249-272. doi: 10.1007/978-1-0716-2237-7_15.

ABSTRACT

Exploiting the statistical associations coming out from a GWAS experiment to identify and validate candidate genes may be potentially difficult and time consuming. To fill the gap between the identification of candidate genes toward their functional validation onto the trait performance, the prioritization of variants underlying the GWAS-associated regions is necessary. In parallel, recent developments in genomics and statistical methods have been achieved notably in human genetic and they are accordingly being adopted in plant breeding toward the study of the genetic architecture of traits to sustain genetic gains. In this chapter, we aim at providing both theoretical and practical aspects underlying three main options including (1) the MetaGWAS analysis, (2) the statistical fine mapping and (3) the integration of functional data toward the identification and validation of candidate genes from a GWAS experiment.

PMID:35641769 | DOI:10.1007/978-1-0716-2237-7_15

Methods Mol Biol. 2022;2481:219-245. doi: 10.1007/978-1-0716-2237-7_14.

ABSTRACT

Genome wide association study (GWAS), which is a powerful tool to detect the relationship between the traits of interest and high-density markers, has provided unprecedented insights into the genetic basis of quantitative variation for complex traits. Along with the development of high-throughput sequencing technology, both sample sizes and marker sizes are increasing rapidly, which make computations more challenging than ever. Therefore, to efficiently process big data with limited computing resources in a reasonable time and to use state-of-the-art statistical models to reduce false positive and false negative rates have always been hot topics in the domain of GWAS. In this chapter, we describe how to perform GWAS using an R package, rMVP, which includes data preparation, evaluation of population structure, association tests by different models, and high-quality visualization of GWAS results.

PMID:35641768 | DOI:10.1007/978-1-0716-2237-7_14