Am J Hematol. 2022 Mar 31. doi: 10.1002/ajh.26551. Online ahead of print.
NO ABSTRACT
PMID:35358346 | DOI:10.1002/ajh.26551
Tag: statistics
Med Phys. 2022 Mar 31. doi: 10.1002/mp.15639. Online ahead of print.
ABSTRACT
PURPOSE: Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V) in combination with various CT scan parameters on the semi-automated quantification using various lung nodules.
METHODS: A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (β) were reported to demonstrate effect size and directionality.
RESULTS: The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (β = 9.000, p<0.001). APE had an inverse relationship with nodule diameter (mm; β = -3.499, p<0.001) and tube current (mA; β = -0.006, p<0.001). MAPE did not vary based on the ASIR-V level (range: 5.7-13.1%).
CONCLUSION: Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction. This article is protected by copyright. All rights reserved.
PMID:35358333 | DOI:10.1002/mp.15639
Spec Care Dentist. 2022 Mar 31. doi: 10.1111/scd.12716. Online ahead of print.
ABSTRACT
AIM: To compare prevalence of oral hygiene status, dental trauma and malocclusion among institutionalized visually impaired (VI) and non-visually impaired (NVI) adolescents in Lagos State, Nigeria.
METHODS AND RESULTS: A cross sectional study conducted among institutionalized VI and NVI adolescents in Lagos State, Nigeria. One hundred and thirty-two randomly selected VI adolescents aged between 9 and 23 years and 138 NVI participants aged between 9 and 17 years were recruited using a multi-stage random sampling technique. Oral examination assessed oral hygiene status, dental trauma and malocclusion. Data entry and analysis was by SPSS version 20. Associations were analyzed using chi-square test. Statistical significance was set at p < .05. Male to female ratio among the VI and NVI participants, were 1.5:1 and 1:3, mean ages were 15.03 ± 3.36 and 12.96 years ± 1.89 SD, prevalence of dental injury were 16.67% and 7.2% and malocclusion were 15.2% and 11.6% respectively. More of the NVI participants had good oral hygiene status (29.0%).
CONCLUSION: Uncomplicated dental injury was more prevalent among the VI male participants than their NVI counterparts while malocclusion was significantly less among the NVI participants. There is an urgent need for the development of policy and protocol on oral health of persons with special needs.
PMID:35358330 | DOI:10.1111/scd.12716
J Pathol. 2022 Mar 31. doi: 10.1002/path.5900. Online ahead of print.
ABSTRACT
A thickened, white patch – leukoplakia – in the oral cavity is usually benign, but sometimes (in ~9% of individuals) progresses to malignant tumour. Because the genomic basis of this progression is poorly understood, we undertook this study and collected samples of four tissues – leukoplakia, tumour, adjacent normal and blood -from each of 28 patients suffering from gingivobuccal oral cancer. We performed multi-omics analysis of the 112 collected tissues (4 tissues per patient from 28 patients) and integrated information on progressive changes in mutational and transcriptional profiles of each patient to create this genomic narrative. Additionally, we generated and analysed whole-exome sequence data from leukoplakia tissues collected from 11 individuals not suffering from oral cancer. Non-synonymous somatic mutations in CASP8 gene were identified as the likely events to initiate malignant transformation, since these were frequently shared between tumour and co-occurring leukoplakia. CASP8 alterations were also shown to enhance expressions of genes that favours lateral spread of mutant cells. During malignant transformation, additional pathogenic mutations are acquired in key genes (TP53, NOTCH1, HRAS) (41% of patients); chromosomal-instability (arm-level deletions of 19p and q, focal-deletion of DNA-repair pathway genes and NOTCH1, amplification of EGFR) (77%) and increased APOBEC-activity (23%) are also observed. These additional alterations were present singly (18% of patients) or in combination (68%). Some of these alterations likely impact on immune-dynamics of the evolving transformed tissue; progression to malignancy is associated with immune suppression through infiltration of regulatory T-cells (56%), depletion of cytotoxic T-cells (68%) and antigen presenting dendritic cells (72%) with concomitant increase in inflammation (92%). Patients can be grouped into three clusters by the estimated time to development of cancer from precancer by acquiring additional mutations (Range: 4-10 years). Our findings provide deep molecular insights into the evolutionary processes and trajectories of oral cancer initiation and progression. This article is protected by copyright. All rights reserved.
PMID:35358331 | DOI:10.1002/path.5900
Cancer Res. 2022 Mar 31:canres.3074.2021. doi: 10.1158/0008-5472.CAN-21-3074. Online ahead of print.
ABSTRACT
Overtreatment remains a pervasive problem in prostate cancer (PCa) management due to the highly variable and often indolent course of disease. Molecular signatures derived from gene expression profiling have played critical roles in guiding PCa treatment decisions. Many gene expression signatures have been developed to improve the risk stratification of PCa and some of them have already been applied to clinical practice. However, no comprehensive evaluation has been performed to compare the performance of these signatures. In this study, we conducted a systematic and unbiased evaluation of 15 machine learning (ML) algorithms and 30 published PCa gene expression-based prognostic signatures leveraging 10 transcriptomics datasets with 1,558 primary PCa patients from public data repositories. This analysis revealed that survival analysis models outperformed binary classification models for risk assessment, and the performance of the survival analysis methods – Cox model regularized with ridge penalty (Cox-Ridge) and partial least squares regression for Cox model (Cox-PLS) – were generally more robust than the other methods. Based on the Cox-Ridge algorithm, several top prognostic signatures displayed comparable or even better performance than commercial panels. These findings will facilitate the identification of existing prognostic signatures that are promising for further validation in prospective studies and promote the development of robust prognostic models to guide clinical decision-making. Moreover, this study provides a valuable data resource from large primary PCa cohorts, which can be used to develop, validate, and evaluate novel statistical methodologies and molecular signatures to improve PCa management.
PMID:35358302 | DOI:10.1158/0008-5472.CAN-21-3074
PLoS One. 2022 Mar 31;17(3):e0266343. doi: 10.1371/journal.pone.0266343. eCollection 2022.
ABSTRACT
BACKGROUND: The “Coronavirus Disease 2019” (COVID-19) pandemic has become a major challenge for all healthcare systems worldwide, and besides generating a high toll of deaths, it has caused economic losses. Hospitals have played a key role in providing services to patients and the volume of hospital activities has been refocused on COVID-19 patients. Other activities have been limited/repurposed or even suspended and hospitals have been operating with reduced capacity. With the decrease in non-COVID-19 activities, their financial system and sustainability have been threatened, with hospitals facing shortage of financial resources. The aim of this study was to investigate the effects of COVID-19 on the revenues of public hospitals in Lorestan province in western Iran, as a case study.
METHOD: In this quasi-experimental study, we conducted the interrupted time series analysis to evaluate COVID-19 induced changes in monthly revenues of 18 public hospitals, from April 2018 to August 2021, in Lorestan, Iran. In doing so, public hospitals report their earnings to the University of Medical Sciences monthly; then, we collected this data through the finance office.
RESULTS: Due to COVID-19, the revenues of public hospitals experienced an average monthly decrease of $172,636 thousand (P-value = 0.01232). For about 13 months, the trend of declining hospital revenues continued. However, after February 2021, a relatively stable increase could be observed, with patient admission and elective surgeries restrictions being lifted. The average monthly income of hospitals increased by $83,574 thousand.
CONCLUSION: COVID-19 has reduced the revenues of public hospitals, which have faced many problems due to the high costs they have incurred. During the crisis, lack of adequate fundings can damage healthcare service delivery, and policymakers should allocate resources to prevent potential shocks.
PMID:35358279 | DOI:10.1371/journal.pone.0266343
Biostatistics. 2022 Mar 31:kxac005. doi: 10.1093/biostatistics/kxac005. Online ahead of print.
ABSTRACT
Integrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets.
PMID:35358296 | DOI:10.1093/biostatistics/kxac005
PLoS One. 2022 Mar 31;17(3):e0266102. doi: 10.1371/journal.pone.0266102. eCollection 2022.
ABSTRACT
Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as a potentially more biologically realistic alternative to backpropagation for training neural networks. This manuscript reviews and extends recent work on the mathematical relationship between predictive coding and backpropagation for training feedforward artificial neural networks on supervised learning tasks. Implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning are discussed along with a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.
PMID:35358258 | DOI:10.1371/journal.pone.0266102
PLoS One. 2022 Mar 31;17(3):e0266203. doi: 10.1371/journal.pone.0266203. eCollection 2022.
ABSTRACT
BACKGROUND: The loss of one or more pregnancies before viability (i.e. pregnancy loss or miscarriage), has been linked to an increased risk of diseases later in life such as myocardial infarction and stroke. Recurrent pregnancy loss (i.e. three consecutive pregnancy losses) and multiple sclerosis have both been linked to immunological traits, which could predispose to both occurrences. The objective of the current study was to investigate if pregnancy loss is associated with later autoimmune neurological disease.
METHODS: This register-based cohort study, included the Danish female population age 12 or older between 1977-2017. Women were grouped hierarchically: 0, 1, 2, ≥3 pregnancy losses, primary recurrent pregnancy loss (i.e. not preceded by a delivery), and secondary recurrent pregnancy loss (i.e. preceded by a delivery). The main outcome was multiple sclerosis and additional outcomes were amyotrophic lateral sclerosis, Guillain-Barré syndrome, and myasthenia gravis. Bayesian Poisson regression estimated incidence rate ratios [IRR] and 95% credible intervals [CI] adjusted for year, age, live births, family history of an outcome, and education.
RESULTS: After 40,380,194 years of follow-up, multiple sclerosis was diagnosed among 7,667 out of 1,513,544 included women (0.5%), median age at diagnosis 34.2 years (IQR 27.4-41.4 years), and median age at symptom onset 31.2 years (IQR 24.8-38.2). The adjusted IRR of multiple sclerosis after 1 pregnancy loss was: 1.03 (95% CI 0.95-1.11), 2 losses: 1.02 (95% CI 0.86-1.20), ≥3 non-consecutive losses: 0.81 (95% CI 0.51-1.24), primary recurrent pregnancy loss: 1.18 (95% CI 0.84-1.60), secondary recurrent pregnancy loss: 1.16 (95% CI 0.81-1.63), as compared to women with no pregnancy losses. Seven sensitivity analyses and analyses for additional outcomes did not show significantly elevated adjusted risk estimates.
CONCLUSIONS: In this nationwide study, pregnancy loss was not significantly associated with autoimmune neurological disorder.
PMID:35358256 | DOI:10.1371/journal.pone.0266203
PLoS One. 2022 Mar 31;17(3):e0266014. doi: 10.1371/journal.pone.0266014. eCollection 2022.
ABSTRACT
The COVID-19 pandemic put dents on every sector of the affected countries, and the informal sector was no exception. This study is based on the quantitative analyses of the primary data of 1,867 informal workers of Bangladesh to shed light on the impact of the pandemic-induced economic crisis on this working class. The survey was conducted between 8 July and 13 August 2020 across the eight administrative divisions of the country. Analysis points out that about ninety percent of these workers faced an income and food expenditure drop during the lockdown. The effect was higher in males, particularly among the urban-centric and educated males engaged in services and sales. The findings suggest that policy support is needed for the informal workers to face such a crisis.
PMID:35358241 | DOI:10.1371/journal.pone.0266014