Tag: pubmed

Mol Pharm. 2022 Jun 7. doi: 10.1021/acs.molpharmaceut.2c00029. Online ahead of print.

ABSTRACT

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

PMID:35671399 | DOI:10.1021/acs.molpharmaceut.2c00029

Biomed Instrum Technol. 2022 Apr 1;56(2):46-57. doi: 10.2345/1943-5967-56.2.46.

ABSTRACT

Manufacturers could improve the pulse tones emitted by pulse oximeters to support more accurate identification of a patient’s peripheral oxygen saturation (SpO2) range. In this article, we outline the strengths and limitations of the variable-pitch tone that represents SpO2 of each detected pulse, and we argue that enhancements to the tone to demarcate clinically relevant ranges are feasible and desirable. The variable-pitch tone is an appreciated and trusted feature of the pulse oximeter’s user interface. However, studies show that it supports relative judgments of SpO2 trends over time and is less effective at supporting absolute judgments about the SpO2 number or conveying when SpO2 moves into clinically important ranges. We outline recent studies that tested whether acoustic enhancements to the current tone could convey clinically important ranges more directly, without necessarily using auditory alarms. The studies cover the use of enhanced variable-pitch pulse oximeter tones for neonatal and adult use. Compared with current tones, the characteristics of the enhanced tones represent improvements that are both clinically relevant and statistically significant. We outline the benefits of enhanced tones, as well as discuss constraints of which developers of enhanced tones should be aware if enhancements are to be successful.

PMID:35671368 | DOI:10.2345/1943-5967-56.2.46

J Chem Theory Comput. 2022 Jun 7. doi: 10.1021/acs.jctc.2c00255. Online ahead of print.

ABSTRACT

Machine learning (ML) approaches have demonstrated the ability to predict molecular spectra at a fraction of the computational cost of traditional theoretical chemistry methods while maintaining high accuracy. Graph neural networks (GNNs) are particularly promising in this regard, but different types of GNNs have not yet been systematically compared. In this work, we benchmark and analyze five different GNNs for the prediction of excitation spectra from the QM9 dataset of organic molecules. We compare the GNN performance in the obvious runtime measurements, prediction accuracy, and analysis of outliers in the test set. Moreover, through TMAP clustering and statistical analysis, we are able to highlight clear hotspots of high prediction errors as well as optimal spectra prediction for molecules with certain functional groups. This in-depth benchmarking and subsequent analysis protocol lays down a recipe for comparing different ML methods and evaluating dataset quality.

PMID:35671364 | DOI:10.1021/acs.jctc.2c00255

Am J Ind Med. 2022 Jun 7. doi: 10.1002/ajim.23399. Online ahead of print.

ABSTRACT

BACKGROUND: Suicide is among the top 10 causes of premature death in the United States. This study provides details on farmer and rancher suicide decedents, including demographic information, mental health status, history of suicidal thoughts and attempts, and circumstances associated with death.

METHODS: Data for this study were obtained from the Centers for Disease Control and Prevention’s National Violent Death Reporting System Restricted Access Database for the years 2003-2018. Descriptive statistics and adjusted odds ratios are presented for farm and nonfarm populations in addition to farm populations by age groups and sex.

RESULTS: This study found that almost half of the farmer suicide decedents were over 65 years old. Firearms were the most widely used method for farmers and ranchers regardless of age and sex. Young farmers and ranchers that died by suicide were more likely to have had relationship problems and older farmers and ranchers that died by suicides were more likely to have had a physical health problem. Male farmer and rancher suicide decedents were more likely to die by firearm than females, and female farmer and rancher suicide decedents were likely to have resided in a small metropolitan area, however, due to small numbers and suppression in the data, most sex comparisons were not able to be presented.

CONCLUSIONS: While no clear risk factor for suicide among farmers and ranchers emerged, results underscore the complex nature of suicide and the need for multifaceted, culturally competent interventions and campaigns that address suicide risk and prevention at the individual and community levels.

PMID:35671362 | DOI:10.1002/ajim.23399

Nurs Forum. 2022 Jun 7. doi: 10.1111/nuf.12760. Online ahead of print.

ABSTRACT

BACKGROUND: Interpretive pedagogy with simulation encourages students to consider multiple perspectives contextually leading students to think deeper in a shared learning environment.

PROBLEM: Clinical sites were lacking in a senior nursing leadership and management course and necessitated the adaptation of traditional clinical teaching methodologies.

APPROACH: Low-fidelity simulation was used as an active learning strategy to fulfill clinical hours.

OUTCOMES: Comparing student groups’ pretest mean scores were not significant (p = .610; 95% confidence interval [CI] [-0.95, 0.12]). Comparatively, the student groups’ posttest scores ranging between 87% and 90%, respectively, were also not statistical significance (p = .136, 95% CI [-0.95, 0.12]).

CONCLUSION: Students were positive about their experience. They appreciated the opportunity to practice what they learned in the classroom in a safe environment. As a result, simulation in a senior nursing leadership course can be successfully used as an alternative to traditional clinical experiences and fulfill clinical hour requirements.

PMID:35671354 | DOI:10.1111/nuf.12760

PLoS One. 2022 Jun 7;17(6):e0269574. doi: 10.1371/journal.pone.0269574. eCollection 2022.

ABSTRACT

BACKGROUND: Risk communication and community engagement are among the key strategies used in response to pandemics. Effective risk communication and community engagement can be achieved when assisted by health learning materials. However, their utilization was not known in Ethiopia. Therefore, the present study aimed to assess the utilization of COVID-19 health learning materials (HLMs), and explore barriers and facilitating factors.

METHODS: A sequential explanatory mixed-methods study consisting of two phases was carried out. The first phase was a cross-sectional survey to assess the utilization of COVID-19 HLMs and their predictors. In this phase, a multistage sampling technique was used to select 530 health workers. A self-administered structured questionnaire was used for data collection. Epi-data manager version 4.6.0.2 and STATA version 16 were used for data entry and analyses, respectively. Descriptive analyses were carried out as necessary. Ordinal logistic regression analyses were done to identify the predictors of COVID-19 HLMs utilization. Phase two is a qualitative study to explore enablers and barriers to COVID-19 HLMs utilization. A judgmental sampling technique was used and 14 key informants were recruited. The collected data were uploaded into Atlas ti version 7.0.71. An inductive process of thematic analysis was employed and the data were coded, categorized, and thematized.

RESULTS: Findings showed that out of the total 530 respondents, 210(39.6%), 117(22.1%), and 203(38.3%) of them never use COVID-19 HLMs, use sometimes, and always, respectively. Health workers’ perceived quality of COVID-19 HLMs [AOR = 6.44 (95% CI: 4.18-9.94)], health workers’ perceived usefulness of COVID-19 HLMs [AOR = 2.82 (95% CI: 1.88-4.22)], working facility [AOR = 1.83 (95% CI: 1.07-3.14)], educational level of the respondents [AOR = 1.73 (95% CI: 1.11-2.72)] and availability of COVID-19 HLMs [AOR = 1.45(95% CI: 1.01-2.08)] had statistically significant association with the utilization status of COVID-19 HLMs. Findings from the qualitative study showed that materials-related factors, and structure and health workers-related factors had influence on HLMs utilization.

CONCLUSIONS: In this study, we found that only a few of the respondents were regularly utilizing COVID-19 HLMs. Perceived quality, usefulness, and availability of HLMs, and health workers’ educational status and working facility determined the level of COVID-19 HLMs utilization. There is a need for giving due attention to HLMs, evaluating their quality, availing them to health facilities, and providing training for health workers.

PMID:35671317 | DOI:10.1371/journal.pone.0269574

Int J Audiol. 2022 Jun 7:1-9. doi: 10.1080/14992027.2022.2084461. Online ahead of print.

ABSTRACT

OBJECTIVES: Hearing loss can seriously impact children’s daily life. This study aims to translate and validate the French versions of the hearing performance questionnaires, SSQ-Parent (for 5-18 years old children), and SSQ-Children (for 11-18 years old children).

DESIGN: This controlled prospective trial was conducted between April and October 2020. The forward-backward translation method was used, and a test-retest procedure was carried out on a case and a control population. Cases had at least 30 dBHL hearing loss.

STUDY SAMPLE: 54 cases (mean age 10.4 years old) and 32 controls (mean age 12.5 years old) answered the SSQ-Parent. 35 cases (mean age 13.1 years old) and 35 controls (mean age 14.3 years old) answered the SSQ-Children.

RESULTS: Spearman’s correlation coefficients between global scores of the test and re-test were 0.91 (p < 0.001) for SSQ-Parent, and 0.89 (p < 0.001) for SSQ-Children. Both tests were discriminant (respectively, global score 57.8 vs 92 p < 0.001, 61.2 vs 92.6 p < 0.001), and internally consistent (Cronbach’s alpha 0.94 and 0.97). Items-global score correlation was satisfactory. ROC curves showed high area under curve for the SSQ-Children (0.990), and SSQ-Parent (0.988).

CONCLUSION: The SSQ-Parent and SSQ-Children revealed excellent statistical properties, and can be used for the evaluation of hearing performance of children.

PMID:35671326 | DOI:10.1080/14992027.2022.2084461

PLoS One. 2022 Jun 7;17(6):e0269573. doi: 10.1371/journal.pone.0269573. eCollection 2022.

ABSTRACT

The COVID-19 pandemic has caused over 500 million cases and over six million deaths globally. From these numbers, over 12 million cases and over 250 thousand deaths have occurred on the African continent as of May 2022. Prevention and surveillance remains the cornerstone of interventions to halt the further spread of COVID-19. Google Health Trends (GHT), a free Internet tool, may be valuable to help anticipate outbreaks, identify disease hotspots, or understand the patterns of disease surveillance. We collected COVID-19 case and death incidence for 54 African countries and obtained averages for four, five-month study periods in 2020-2021. Average case and death incidences were calculated during these four time periods to measure disease severity. We used GHT to characterize COVID-19 incidence across Africa, collecting numbers of searches from GHT related to COVID-19 using four terms: ‘coronavirus’, ‘coronavirus symptoms’, ‘COVID19’, and ‘pandemic’. The terms were related to weekly COVID-19 case incidences for the entire study period via multiple linear and weighted linear regression analyses. We also assembled 72 variables assessing Internet accessibility, demographics, economics, health, and others, for each country, to summarize potential mechanisms linking GHT searches and COVID-19 incidence. COVID-19 burden in Africa increased steadily during the study period. Important increases for COVID-19 death incidence were observed for Seychelles and Tunisia. Our study demonstrated a weak correlation between GHT and COVID-19 incidence for most African countries. Several variables seemed useful in explaining the pattern of GHT statistics and their relationship to COVID-19 including: log of average weekly cases, log of cumulative total deaths, and log of fixed total number of broadband subscriptions in a country. Apparently, GHT may best be used for surveillance of diseases that are diagnosed more consistently. Overall, GHT-based surveillance showed little applicability in the studied countries. GHT for an ongoing epidemic might be useful in specific situations, such as when countries have significant levels of infection with low variability. Future studies might assess the algorithm in different epidemic contexts.

PMID:35671301 | DOI:10.1371/journal.pone.0269573

PLoS Genet. 2022 Jun 7;18(6):e1010252. doi: 10.1371/journal.pgen.1010252. Online ahead of print.

ABSTRACT

De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD.

PMID:35671298 | DOI:10.1371/journal.pgen.1010252

PLoS Comput Biol. 2022 Jun 7;18(6):e1009773. doi: 10.1371/journal.pcbi.1009773. Online ahead of print.

ABSTRACT

BACKGROUND: Transcription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive.

METHODOLOGY/PRINCIPAL FINDINGS: This study presents a computational tool, named “Grit”, for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Student’s t-test statistical method that detects high-scoring binding sites utilizing conservation information among species. The program performs sequence scanning at a rate of 3.2 Mbp/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It significantly outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets.

SIGNIFICANCE: Grit is a good alternative to current available motif scanning tools.

PMID:35671296 | DOI:10.1371/journal.pcbi.1009773