Category: Statistics

Sci Rep. 2024 May 11;14(1):10810. doi: 10.1038/s41598-024-61659-2.

ABSTRACT

In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields.

PMID:38734768 | DOI:10.1038/s41598-024-61659-2

Sci Rep. 2024 May 11;14(1):10792. doi: 10.1038/s41598-024-61338-2.

ABSTRACT

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

PMID:38734752 | DOI:10.1038/s41598-024-61338-2

Sci Rep. 2024 May 11;14(1):10816. doi: 10.1038/s41598-024-61382-y.

ABSTRACT

r, s, t-spherical fuzzy (r, s, t-SPF) sets provide a robust framework for managing uncertainties in decision-making, surpassing other fuzzy sets in their ability to accommodate diverse uncertainties through the incorporation of flexible parameters r, s, and t. Considering these characteristics, this article explores sine trigonometric laws to enhance the applicability and theoretical foundation for r, s, t-SPF setting. Following these laws, several aggregation operators (AOs) are designed for aggregation of the r, s, t-SPF data. Meanwhile, the desired characteristics and relationships of these operators are studied under sine trigonometric functions. Furthermore, we build a group decision-making algorithm for addressing multiple attribute group decision-making (MAGDM) problems using the developed AOs. To exemplify the applicability of the proposed algorithm, we address a practical example regarding laptop selection. Finally, parameter analysis and a comprehensive comparison with existing operators are conducted to uncover the superiority and validity of the presented AOs.

PMID:38734743 | DOI:10.1038/s41598-024-61382-y

Sci Rep. 2024 May 11;14(1):10790. doi: 10.1038/s41598-024-61388-6.

ABSTRACT

In this two-center prospective cohort study of children on ECMO, we assessed a panel of plasma brain injury biomarkers using exploratory factor analysis (EFA) to evaluate their interplay and association with outcomes. Biomarker concentrations were measured daily for the first 3 days of ECMO support in 95 participants. Unfavorable composite outcome was defined as in-hospital mortality or discharge Pediatric Cerebral Performance Category > 2 with decline ≥ 1 point from baseline. EFA grouped 11 biomarkers into three factors. Factor 1 comprised markers of cellular brain injury (NSE, BDNF, GFAP, S100β, MCP1, VILIP-1, neurogranin); Factor 2 comprised markers related to vascular processes (vWF, PDGFRβ, NPTX1); and Factor 3 comprised the BDNF/MMP-9 cellular pathway. Multivariable logistic models demonstrated that higher Factor 1 and 2 scores were associated with higher odds of unfavorable outcome (adjusted OR 2.88 [1.61, 5.66] and 1.89 [1.12, 3.43], respectively). Conversely, higher Factor 3 scores were associated with lower odds of unfavorable outcome (adjusted OR 0.54 [0.31, 0.88]), which is biologically plausible given the role of BDNF in neuroplasticity. Application of EFA on plasma brain injury biomarkers in children on ECMO yielded grouping of biomarkers into three factors that were significantly associated with unfavorable outcome, suggesting future potential as prognostic instruments.

PMID:38734737 | DOI:10.1038/s41598-024-61388-6

Lab Med. 2024 May 11:lmae031. doi: 10.1093/labmed/lmae031. Online ahead of print.

NO ABSTRACT

PMID:38733624 | DOI:10.1093/labmed/lmae031

Eur J Prev Cardiol. 2024 May 11:zwae160. doi: 10.1093/eurjpc/zwae160. Online ahead of print.

NO ABSTRACT

PMID:38733621 | DOI:10.1093/eurjpc/zwae160

Cancer. 2024 May 11. doi: 10.1002/cncr.35356. Online ahead of print.

ABSTRACT

INTRODUCTION: Cancer risk factors are more common among sexual minority populations (e.g., lesbian, bisexual) than their heterosexual peers, yet little is known about cancer incidence across sexual orientation groups.

METHODS: The 1989-2017 data from the Nurses’ Health Study II, a longitudinal cohort of female nurses across the United States, were analyzed (N = 101,543). Sexual orientation-related cancer disparities were quantified by comparing any cancer incidence among four sexual minority groups based on self-disclosure-(1) heterosexual with past same-sex attractions/partners/identity; (2) mostly heterosexual; (3) bisexual; and (4) lesbian women-to completely heterosexual women using age-adjusted incidence rate ratios (aIRR) calculated by the Mantel-Haenszel method. Additionally, subanalyses at 21 cancer disease sites (e.g., breast, colon/rectum) were conducted.

RESULTS: For all-cancer analyses, there were no statistically significant differences in cancer incidence at the 5% type I error cutoff among sexual minority groups when compared to completely heterosexual women; the aIRR was 1.17 (95% CI,0.99-1.38) among lesbian women and 0.80 (0.58-1.10) among bisexual women. For the site-specific analyses, incidences at multiple sites were significantly higher among lesbian women compared to completely heterosexual women: thyroid cancer (aIRR, 1.87 [1.03-3.41]), basal cell carcinoma (aIRR, 1.85 [1.09-3.14]), and non-Hodgkin lymphoma (aIRR, 2.13 [1.10-4.12]).

CONCLUSION: Lesbian women may be disproportionately burdened by cancer relative to their heterosexual peers. Sexual minority populations must be explicitly included in cancer prevention efforts. Comprehensive and standardized sexual orientation data must be systematically collected so nuanced sexual orientation-related cancer disparities can be accurately assessed for both common and rare cancers.

PMID:38733613 | DOI:10.1002/cncr.35356

J Magn Reson Imaging. 2024 May 11. doi: 10.1002/jmri.29396. Online ahead of print.

ABSTRACT

BACKGROUND: The use of peritumoral features to determine the survival time of patients with rectal cancer (RC) is still imprecise.

PURPOSE: To explore the correlation between intratumoral, peritumoral and combined features, and overall survival (OS).

STUDY TYPE: Retrospective.

POPULATION: One hundred sixty-six RC patients (53 women, 113 men; average age: 55 ± 12 years) who underwent radical resection after neoadjuvant therapy.

FIELD STRENGTH/SEQUENCE: 3 T; T₂WI sagittal, T₁WI axial, T₂WI axial with fat suppression, and high-resolution T₂WI axial sequences, enhanced T₁WI axial and sagittal sequences with fat suppression.

ASSESSMENT: Radiologist A segmented 166 patients, and radiologist B randomly segmented 30 patients. Intratumoral and peritumoral features were extracted, and features with good stability (ICC ≥0.75) were retained through intra-observer analysis. Seven classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Extremely randomized trees (ET), eXtreme Gradient Boosting (XGBoost), and LightGBM (LGBM), were applied to select the classifier with the best performance. Next, the Rad-score of best classifier and the clinical features were selected to establish the models, thus, nomogram was built to identify the association with 1-, 3-, and 5-year OS.

STATISTICAL TESTS: LASSO, regression analysis, ROC, DeLong method, Kaplan-Meier curve. P < 0.05 indicated a significant difference.

RESULTS: Only Node (irregular tumor nodules in the surrounding mesentery) and ExtraMRF (lymph nodes outside the perirectal mesentery) were significantly different in 20 clinical features. Twelve intratumoral, 3 peritumoral, and 14 combined features related to OS were selected. LR, SVM, and RF classier showed the best efficacy in the intratumoral, peritumoral, and combined model, respectively. The combined model (AUC = 0.954 and 0.821) had better survival association than the intratumoral model (AUC = 0.833 and 0.813) and the peritumoral model (AUC = 0.824 and 0.687).

DATA CONCLUSION: The proposed peritumoral model with radiomics features may serve as a tool to improve estimated survival time.

EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

PMID:38733601 | DOI:10.1002/jmri.29396

Int J Med Robot. 2024 Jun;20(3):e2635. doi: 10.1002/rcs.2635.

ABSTRACT

BACKGROUND: Rotational alignment in total knee arthroplasty (TKA) is a crucial technical point that needs attention. We conducted a retrospective study to investigate whether a new robot-assisted TKA (RA-TKA) could improve the accuracy of rotational alignment and whether rotational alignment affects postoperative pain and functional evaluation of the knee.

METHODS: A total of 136 consecutive patients who underwent TKA were included in this study. Half of the patients underwent RA-TKA and the other half underwent conventional TKA (CON-TKA) by the same group of surgeons. Collect the relevant parameters.

RESULTS: The postoperative femoral rotation angle (FRA) was -0.72 ± 2.59° in the robot-assisted group and 1.13 ± 2.73° in the conventional group, and were statistically significantly different (p < 0.001).

CONCLUSION: This study provides preliminary evidence that the RA-TKA provides more precise control of FRA than CON-TKA, and verifies that tibial rotation angle and combined rotation angle affect postoperative knee pain and functional evaluation.

PMID:38733580 | DOI:10.1002/rcs.2635

Syst Biol. 2024 May 11:syae024. doi: 10.1093/sysbio/syae024. Online ahead of print.

ABSTRACT

Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared to haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared to using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical non-identifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

PMID:38733563 | DOI:10.1093/sysbio/syae024