Tag: nevin manimala

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

PLoS One. 2022 Mar 31;17(3):e0266002. doi: 10.1371/journal.pone.0266002. eCollection 2022.

ABSTRACT

Northern Sweden has been the object of intense metal mining in the last decades producing several water-filled open-pits, or pit lakes. Most of these pit lakes have been limed to maintain a good water quality and to prevent generation of acidic water that could leach the exposed rocks and release metals into water. The aim of this work was to examine the concentration of stable elements and naturally occurring radionuclides in water and sediment samples from pit lakes originating from non-uranium mining activities in Northern Sweden. Surface water and surface sediments were collected from 27 pit lakes in Northern Sweden. Water quality parameters, concentration of stable elements and radionuclides were measured by a water probe, ICP-MS and XRF, and alpha and gamma spectrometry, respectively. Furthermore, a multivariate statistical analysis (PCA) was performed on the water samples and sediments. In general, the quality of the surface water was good, but some lakes had low pH values (2.5-5.7), and high concentrations of Fe (up to 200 mg/L) and other metals (e.g. Zn, Cu). When relating the metal concentrations in sediments in pit lakes with the concentration found in natural lakes, some sites had relatively high levels of Cu, As, Cr and Pb. The activity concentration of 210Po, and U and Th isotopes in water and sediment samples were at environmental levels, as was the ambient dose equivalent rate at these sites (range 0.08-0.14 μSv/h).

PMID:35358244 | DOI:10.1371/journal.pone.0266002

PLoS One. 2022 Mar 31;17(3):e0265529. doi: 10.1371/journal.pone.0265529. eCollection 2022.

ABSTRACT

PURPOSE: The COVID-19 pandemic has spread worldwide, and almost 396 million people have been infected around the globe. Latin American countries have been deeply affected, and there is a lack of data in this regard. This study aims to identify the clinical characteristics, in-hospital outcomes, and factors associated with ICU admission due to COVID-19. Furthermore, to describe the functional status of patients at hospital discharge after the acute episode of COVID-19.

MATERIAL AND METHODS: This was a prospective, multicenter, multinational observational cohort study of subjects admitted to 22 hospitals within Latin America. Data were collected prospectively. Descriptive statistics were used to characterize patients, and multivariate regression was carried out to identify factors associated with severe COVID-19.

RESULTS: A total of 3008 patients were included in the study. A total of 64.3% of patients had severe COVID-19 and were admitted to the ICU. Patients admitted to the ICU had a higher mean (SD) 4C score (10 [3] vs. 7 [3)], p<0.001). The risk factors independently associated with progression to ICU admission were age, shortness of breath, and obesity. In-hospital mortality was 24.1%, whereas the ICU mortality rate was 35.1%. Most patients had equal self-care ability at discharge 43.8%; however, ICU patients had worse self-care ability at hospital discharge (25.7% [497/1934] vs. 3.7% [40/1074], p<0.001).

CONCLUSIONS: This study confirms that patients with SARS CoV-2 in the Latin American population had a lower mortality rate than previously reported. Systemic complications are frequent in patients admitted to the ICU due to COVID-19, as previously described in high-income countries.

PMID:35358238 | DOI:10.1371/journal.pone.0265529

PLoS One. 2022 Mar 31;17(3):e0265895. doi: 10.1371/journal.pone.0265895. eCollection 2022.

ABSTRACT

BACKGROUND: Non-invasive finger-cuff monitors measuring cardiac index and vascular tone (SVRI) classify emergency department (ED) patients with acute heart failure (AHF) into three otherwise-indistinguishable subgroups. Our goals were to validate these “hemodynamic profiles” in an external cohort and assess their association with clinical outcomes.

METHODS: AHF patients (n = 257) from five EDs were prospectively enrolled in the validation cohort (VC). Cardiac index and SVRI were measured with a ClearSight finger-cuff monitor (formerly NexFin, Edwards Lifesciences) as in a previous study (derivation cohort, DC, n = 127). A control cohort (CC, n = 127) of ED patients with sepsis was drawn from the same study as the DC. K-means cluster analysis previously derived two-dimensional (cardiac index and SVRI) hemodynamic profiles in the DC and CC (k = 3 profiles each). The VC was subgrouped de novo into three analogous profiles by unsupervised K-means consensus clustering. PERMANOVA tested whether VC profiles 1-3 differed from profiles 1-3 in the DC and CC, by multivariate group composition of cardiac index and vascular tone. Profiles in the VC were compared by a primary outcome of 90-day mortality and a 30-day ranked composite secondary outcome (death, mechanical cardiac support, intubation, new/emergent dialysis, coronary intervention/surgery) as time-to-event (survival analysis) and binary events (odds ratio, OR). Descriptive statistics were used to compare profiles by two validated risk scores for the primary outcome, and one validated score for the secondary outcome.

RESULTS: The VC had median age 60 years (interquartile range {49-67}), and was 45% (n = 116) female. Multivariate profile composition by cardiac index and vascular tone differed significantly between VC profiles 1-3 and CC profiles 1-3 (p = 0.001, R2 = 0.159). A difference was not detected between profiles in the VC vs. the DC (p = 0.59, R2 = 0.016). VC profile 3 had worse 90-day survival than profiles 1 or 2 (HR = 4.8, 95%CI 1.4-17.1). The ranked secondary outcome was more likely in profile 1 (OR = 10.0, 1.2-81.2) and profile 3 (12.8, 1.7-97.9) compared to profile 2. Diabetes prevalence and blood urea nitrogen were lower in the high-risk profile 3 (p<0.05). No significant differences between profiles were observed for other clinical variables or the 3 clinical risk scores.

CONCLUSIONS: Hemodynamic profiles in ED patients with AHF, by non-invasive finger-cuff monitoring of cardiac index and vascular tone, were replicated de novo in an external cohort. Profiles showed significantly different risks of clinically-important adverse patient outcomes.

PMID:35358231 | DOI:10.1371/journal.pone.0265895

PLoS One. 2022 Mar 31;17(3):e0265499. doi: 10.1371/journal.pone.0265499. eCollection 2022.

ABSTRACT

BACKGROUND: Tuberculosis lymphadenitis (TBLN) diagnosis is often challenging in most resource poor settings. Often cytopathologic diagnosis of TBLN suspected patients is inconclusive impeding timely clinical management of TBLN suspected patients, further exposing suspected patients either for unnecessary use of antibiotics or empirical treatment. This may lead to inappropriate treatment outcome or more suffering of suspected patients from the disease. In this study, an integrated diagnostic approach has been evaluated to elucidate its utility in the identification of TBLN suspected patients.

METHODS: A cross-sectional study was conducted on 96 clinically diagnosed TBLN suspected patients, where fine needle aspirate (FNA) samples were collected at the time of diagnosis. FNA cytology, Ziehl-Neelsen (ZN), Auramine O (AO) staining, GeneXpert MTB/RIF and Real time PCR (RT-PCR) were performed on concentrated FNA samples. Considering culture as a gold standard, the sensitivity, specificity, positive and negative predictive values were calculated. Cohen’s Kappa value was used to measure interrater variability and level of agreement and a P-value of <0.05 was considered as statistically significant.

RESULT: Out of the 96 FNA sample, 12 (12.5%) were identified to have Mycobacterium tuberculosis (Mtb) using ZN staining, 27 (28.1%) using AO staining, 51 (53.2%) using FNAC, 43 (44.7%) using GeneXpert MTB/RIF, 51 (53.1%) using Real time PCR (RT-PCR) and 36 (37.5%) using Lowenstein-Jensen (LJ) culture. Compared to LJ culture, the sensitivities of GeneXpert MTB/RIF, RT-PCR, and FNAC were 91.7%, 97.2%, and 97.2%, respectively and the specificities were 83.3%, 73.3%, and 68.3%, respectively. GeneXpert MTB/RIF and RT-PCR when combined with FNAC detected 61 (63.5%) cases as having Mtb, and the sensitivity and specificity was 100% and 58.3%, respectively.

CONCLUSION: FNA cytology and RT-PCR detected more TBLN cases compared to other Mtb detection tools and the detection sensitivity even improved when FNA cytology was combined with GeneXpert MTB/RIF, performed on concentrated FNA sample, suggesting the combined tests as an alternative approach for improved diagnosis of TBLN.

PMID:35358212 | DOI:10.1371/journal.pone.0265499