Nevin Manimala

IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul 18;PP. doi: 10.1109/TCBB.2022.3191972. Online ahead of print.

ABSTRACT

Numerous studies have reported that micro RNAs (miRNAs) play pivotal roles in disease pathogenesis based on the deregulation of the expressions of target messenger RNAs. Therefore, the identification of disease-related miRNAs is of great significance in understanding human complex diseases, which can also provide insight into the design of novel prognostic markers and disease therapies. Considering the time and cost involved in wet experiments, most recent works have focused on the effective and feasible modeling of computational frameworks to uncover miRNA-disease associations. In this study, we propose a novel framework called node2vec-based neural collaborative filtering for predicting miRNA-disease association (NCMD) based on deep neural networks. Initially, NCMD exploits Node2vec to learn low-dimensional vector representations of miRNAs and diseases. Next, it utilizes a deep learning framework that combines the linear ability of generalized matrix factorization and nonlinear ability of a multilayer perceptron. Experimental results clearly demonstrate the comparable performance of NCMD relative to the state-of-the-art methods according to statistical measures. In addition, case studies on breast cancer, lung cancer and pancreatic cancer validate the effectiveness of NCMD. Extensive experiments demonstrate the benefits of modeling a neural collaborative-filtering-based approach for discovering novel miRNA-disease associations.

PMID:35849666 | DOI:10.1109/TCBB.2022.3191972

IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul 18;PP. doi: 10.1109/TCBB.2022.3191395. Online ahead of print.

ABSTRACT

Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model. The IGPRED-Multitask method is available at PSP server, which can be accessed by visiting http://psp.agu.edu.tr.

PMID:35849663 | DOI:10.1109/TCBB.2022.3191395

PLoS Negl Trop Dis. 2022 Jul 18;16(7):e0010622. doi: 10.1371/journal.pntd.0010622. Online ahead of print.

ABSTRACT

BACKGROUND: The results of the latest national survey on important human parasitic diseases in 2015-2016 showed Guangdong Province is still a moderately endemic area, with the weighted prevalence of soil-transmitted helminths (STHs) higher than national average. High-resolution age- and gender-specific spatial-temporal risk maps can support the prevention and control of STHs, but not yet available in Guangdong.

METHODOLOGY: Georeferenced age- and gender-specific disease data of STH infections in Guangdong Province was derived from three national surveys on important human parasitic diseases, conducted in 1988-1992, 2002-2003, and 2015-2016, respectively. Potential influencing factors (e.g., environmental and socioeconomic factors) were collected from open-access databases. Bayesian geostatistical models were developed to analyze the above data, based on which, high-resolution maps depicting the STH infection risk were produced in the three survey years in Guangdong Province.

PRINCIPAL FINDINGS: There were 120, 31, 71 survey locations in the first, second, and third national survey in Guangdong, respectively. The overall population-weighted prevalence of STH infections decreased significantly over time, from 68.66% (95% Bayesian credible interval, BCI: 64.51-73.06%) in 1988-1992 to 0.97% (95% BCI: 0.69-1.49%) in 2015-2016. In 2015-2016, only low to moderate infection risk were found across Guangdong, with hookworm becoming the dominant species. Areas with relatively higher risk were (>5%) mostly distributed in the western region. Females had higher infection risk of STHs than males. The infection risk of A. lumbricoides and T. trichiura were higher in children, while middle-aged and elderly people had higher infection risk of hookworm. Precipitation, elevation, land cover, and human influence index (HII) were significantly related with STH infection risk.

CONCLUSIONS/SIGNIFICANCE: We produced the high-resolution, age- and gender-specific risk maps of STH infections in the three national survey periods across nearly 30 years in Guangdong Province, which can provide important information assisting the control and prevention strategies.

PMID:35849623 | DOI:10.1371/journal.pntd.0010622

PLoS Comput Biol. 2022 Jul 18;18(7):e1010287. doi: 10.1371/journal.pcbi.1010287. Online ahead of print.

ABSTRACT

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

PMID:35849618 | DOI:10.1371/journal.pcbi.1010287

PLoS One. 2022 Jul 18;17(7):e0271216. doi: 10.1371/journal.pone.0271216. eCollection 2022.

ABSTRACT

BACKGROUND: Globally, postpartum hemorrhage is the leading preventable cause of maternal mortality. To decrease postpartum hemorrhage-related maternal mortalities, identifying its risk factors is crucial to suggest interventions. In this regard, little is known about the link between primary postpartum hemorrhage and inter-pregnancy interval in Ethiopia, where more than half of pregnancies occur shortly after the preceding childbirth. Therefore, we aimed to elucidate the association of primary postpartum hemorrhage with an inter-pregnancy interval in urban South Ethiopia.

METHODS: A community-based matched nested case-control study was conducted among a cohort of 2548 pregnant women. All women with primary postpartum hemorrhage during the follow-up (n = 73) were taken as cases. Women who were randomly selected from those without primary postpartum hemorrhage (n = 292) were taken as controls. Cases were individually matched with controls (1:4 ratio) for age group and location. A conditional logistic regression analysis was done using R version 4.0.5 software. Statistically, a significant association was declared using 95% CI and p-value. Attributable fraction (AF) and population attributable fraction (PAF) were used to estimate the public health impacts of the inter-pregnancy interval.

RESULTS: This study found out that more than half (66%) of primary postpartum hemorrhage was attributed to inter-pregnancy interval <24 months (AF = 66.3%, 95% CI: 37.5, 82.5%). This could be prevented if the inter-pregnancy interval was increased to 24-60 months. Likewise, nearly half (49%) of primary postpartum hemorrhage in the study population could be prevented if the inter-pregnancy interval <24 months was prevented. Additionally, primary postpartum hemorrhage was attributed to antepartum hemorrhage, prolonged labour and multiple pregnancies.

CONCLUSIONS: Primary postpartum hemorrhage was associated with inter-pregnancy interval under 24 months, highlighting the need to improve postpartum modern contraceptive utilization in the community. Counseling couples about how long to wait until subsequent pregnancy and the risk when the inter-pregnancy interval is short need to be underlined.

PMID:35849596 | DOI:10.1371/journal.pone.0271216

PLoS One. 2022 Jul 18;17(7):e0265368. doi: 10.1371/journal.pone.0265368. eCollection 2022.

ABSTRACT

Common statistical modeling methods do not necessarily produce the most relevant or interpretable effect estimates to communicate risk. Overreliance on the odds ratio and relative effect measures limit the potential impact of epidemiologic and public health research. We created a straightforward R package, called riskCommunicator, to facilitate the presentation of a variety of effect measures, including risk differences and ratios, number needed to treat, incidence rate differences and ratios, and mean differences. The riskCommunicator package uses g-computation with parametric regression models and bootstrapping for confidence intervals to estimate effect measures in time-fixed data. We demonstrate the utility of the package using data from the Framingham Heart Study to estimate the effect of prevalent diabetes on the 24-year risk of cardiovascular disease or death. The package promotes the communication of public-health relevant effects and is accessible to a broad range of epidemiologists and health researchers with little to no expertise in causal inference methods or advanced coding.

PMID:35849588 | DOI:10.1371/journal.pone.0265368

PLoS Negl Trop Dis. 2022 Jul 18;16(7):e0010604. doi: 10.1371/journal.pntd.0010604. eCollection 2022 Jul.

ABSTRACT

BACKGROUND: Patients with rheumatic heart disease (RHD) and congestive cardiac failure (CCF) are believed to have an increased risk of melioidosis and are thought to be more likely to die from the infection. This study was performed to confirm these findings in a region with a high incidence of all three conditions.

PRINCIPAL FINDINGS: Between January 1998 and December 2021 there were 392 cases of melioidosis in Far North Queensland, tropical Australia; 200/392 (51.0%) identified as an Indigenous Australian, and 337/392 (86.0%) had a confirmed predisposing comorbidity that increased risk for the infection. Overall, 46/392 (11.7%) died before hospital discharge; the case fatality rate declining during the study period (p for trend = 0.001). There were only 3/392 (0.8%) with confirmed RHD, all of whom had at least one other risk factor for melioidosis; all 3 survived to hospital discharge. Among the 200 Indigenous Australians in the cohort, 2 had confirmed RHD; not statistically greater than the prevalence of RHD in the local general Indigenous population (1.0% versus 1.2%, p = 1.0). RHD was present in only 1/193 (0.5%) cases of melioidosis diagnosed after October 2016, a period which coincided with prospective data collection. There were 26/392 (6.6%) with confirmed CCF, but all 26 had another traditional risk factor for melioidosis. Patients with CCF were more likely to also have chronic lung disease (OR (95% CI: 4.46 (1.93-10.31), p<0.001) and chronic kidney disease (odds ratio (OR) (95% confidence interval (CI): 2.98 (1.22-7.29), p = 0.01) than those who did not have CCF. Two patients with melioidosis and CCF died before hospital discharge; both were elderly (aged 81 and 91 years) and had significant comorbidity.

CONCLUSIONS: In this region of tropical Australia RHD and CCF do not appear to be independent risk factors for melioidosis and have limited prognostic utility.

PMID:35849581 | DOI:10.1371/journal.pntd.0010604

PLoS One. 2022 Jul 18;17(7):e0270914. doi: 10.1371/journal.pone.0270914. eCollection 2022.

ABSTRACT

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016-2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9-1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59-84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis-Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of ‘Long COVID-19’ symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography-Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.

PMID:35849572 | DOI:10.1371/journal.pone.0270914

PLoS One. 2022 Jul 18;17(7):e0270886. doi: 10.1371/journal.pone.0270886. eCollection 2022.

ABSTRACT

BACKGROUND: The Joint United Nations Programme on AIDS (UNAIDS) has emphasized on the incidence-prevalence ratio (IPR) and incidence-mortality ratio (IMR) to measure the progress in HIV epidemic control. In this paper, we describe the status of epidemic control in India and in various states in terms of UNAIDS’s recommended metrices.

METHOD: The National AIDS Control Programme (NACP) of India spearheads work on mathematical modelling to estimate HIV burden based on periodically conducted sentinel surveillance for providing guidance to program implementation and policymaking. Using the results of the latest round of HIV Estimations in 2019, IPR and IMR were calculated.

RESULTS: National level IPR was 0.029 [0.022-0.037] in 2019 and ranged from 0.01 to 0.15 in various States and Union Territories (UTs). Corresponding Incidence-Mortality Ratio was at 0.881 [0.754-1.014] nationally and ranged between 0.20 and 12.90 across the States/UTs.

CONCLUSIONS: Based on UNAIDS recommended indicators for HIV epidemic control, namely IPR and IMR; national AIDS response in India appears on track. However, the program success is not uniform and significant heterogeneity as well as expanding epidemic was observed at the level of States or UTs. Reinforcing States/UTs specific and focused HIV prevention, testing and treatment initiatives may help in the attainment of 2030 Sustainable Development Goals of ending AIDS as a public health threat by 2030.

PMID:35849570 | DOI:10.1371/journal.pone.0270886

PLoS One. 2022 Jul 18;17(7):e0268189. doi: 10.1371/journal.pone.0268189. eCollection 2022.

ABSTRACT

Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 11 locations over three cropping seasons in the cassava breeding program of IITA based in Nigeria to quantify the GEI effects for root quality and yield-related traits. Genetic correlation coefficients and heritability estimates among environments found mostly intermediate to high values indicating high correlations with the major TPE. There was a differential clonal ranking among the environments indicating the existence of GEI as also revealed by the likelihood ratio test (LRT), which further confirmed the statistical model with the heterogeneity of error variances across the environments fit better. For all fitted models, we found the main effects of environment, genotype, and interaction significant for all observed traits except for dry matter content whose GEI sensitivity was marginally significant as found using the FW model. We identified TMS14F1297P0019 and TMEB419 as two topmost stable clones with a sensitivity values of 0.63 and 0.66 respectively using the FW model. However, GGE and AMMI stability value in conjunction with genotype selection index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones combining both stability and yield performance measures. The AMMI-2 model clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield. Alternatively, we identified 3 clusters of testing environments based on genotypic BLUPs derived from the random GEI component.

PMID:35849556 | DOI:10.1371/journal.pone.0268189