Tag: pubmed

Biometrics. 2021 Dec 29. doi: 10.1111/biom.13617. Online ahead of print.

ABSTRACT

”For how many days during the past 30 days was your mental health not good?” The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: the data are overdispersed, zero-inflated, bounded by 30, and heaped in five- and seven-day increments. To address these challenges-which are especially common for health questionnaire data-we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (star) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an EM algorithm that is compatible with any continuous data model estimable by least squares. star regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. Using star regression, we identify key factors associated with self-reported mental health and demonstrate substantial improvements in goodness-of-fit compared to existing count data regression models.

PMID:34965306 | DOI:10.1111/biom.13617

Arch Prev Riesgos Labor. 2021 Oct 15;24(4):370-382. doi: 10.12961/aprl.2021.24.04.04.

ABSTRACT

OBJECTIVE: Frontline healthcare workers are the first line of defense against Covid-19, resulting in a higher risk of infection. The objective of this study was to describe the impact of the SARS-CoV-2 infection and its associated risk factors among professionals working in a healthcare consortium that includes different centers.

METHODS: This was a retrospective analytical observational study of 2620 healthcare workers; the project period began with the declaration of the state of alarm in Spain (March 15, 2020) and ended on June 21, 2020. We estimated associations between the independent variables sex, age, seniority, professional category and work location and confirmed COVID-19 as the outcome variable. Bivariate study analysis was based on chi-square test and simple logistic regression with calculation of the odds ratio (OR) and 95% confidence interval (95%CI). Multivariate analysis was performed using multiple logistic regression. Statistical significance was set at p ≤0.05.

RESULTS: All frontline healthcare worker categories were at higher risk than non-patient-facing personnel. Nurses had the highest risk [OR, 14.03 (3.19-61.66)]. With respect to work location, and as compared to non-patient-facing personnel, working in the surgical-medical-hospitalization-clinic [OR 13.43 (1.7-106.12)] and socio-health center [OR 17.77 (2.19-144.04) posed the greatest risks.

CONCLUSIONS: The greatest risk of acquiring COVID-19 was among patient-facing healthcare professionals working in areas where COVID-19 was detected among patients admitted for other pathologies. This risk was higher than in those areas designated for the care of COVID-19 patients, possibly due to differences in the use of personal protective equipment.

PMID:34965326 | DOI:10.12961/aprl.2021.24.04.04

PLoS One. 2021 Dec 29;16(12):e0260529. doi: 10.1371/journal.pone.0260529. eCollection 2021.

ABSTRACT

A stable and rewarding love relationship is considered a key ingredient for happiness in Western culture. Building a successful long-term relationship can be viewed as a control engineering problem, where the control variable is the effort to be made to keep the relationship alive and well. We introduce a new mathematical model for the effort control problem of a couple in love who wants to stay together forever. The problem can be naturally formulated as a dynamic game in continuous time with nonlinearities. Adopting a dynamic programming approach, a tractable computational formulation of the problem is proposed together with an accompanying algorithm to find numerical solutions of the couple’s effort problem. The computational analysis of the model is used to explore feeling trajectories, effort control paths, happiness, and stabilization mechanisms for different types of successful couples. In particular, the simulation analysis provides insight into the pattern of change of both marital quality and effort making in intact marriages and how they are affected by certain level of heterogamy in the couple.

PMID:34965275 | DOI:10.1371/journal.pone.0260529

J Oral Implantol. 2021 Dec 29. doi: 10.1563/aaid-joi-D-21-00219. Online ahead of print.

ABSTRACT

Purpose The aim of this work is to assess the clinical outcomes of implants placed after a nasal floor elevation procedure. Methods A systematic review was conducted using four electronic databases; Medline (Pubmed), Cochrane library, DOAJ and SCOPUS, following the PRISMA statement recommendations to answer the PICO question: “In patients undergoing dental implant placement in the maxillary anterior region (P), Do implants placed after nasal floor elevation (I) have a different survival (O) from those implants placed without grafting procedures (C)?. The study was pre-registered in PROSPERO (CRD42021229479). Included articles quality was assessed using the “NIH quality assessment tool”, “The Newcastle-Ottawa scale” and “JBI critical appraisal tools for case reports”. Results Twelve articles were finally selected, including 151 patients and 460 implants. The weighted mean follow-up was 32.2 months, and the weighted survival rate after this period was 97.64% (range 89.2-100%). No statistical differences could be inferred between the treatments performed in one-stage or two-stage, following a lateral approach or a transcrestal approach or using different grafting materials. A great heterogeneity was found in terms of study design and methodological aspects. For this reason, a quantitative analysis followed by meta-analysis was not possible. Conclusion Within the limitations of this study, implants placed after a nasal floor elevation present a good survival and a low range of complications. In absence of randomized studies, the level of evidence was low, attending the GRADE system and based on the study quality level, the strength of evidence attending the SORT taxonomy was B.

PMID:34965298 | DOI:10.1563/aaid-joi-D-21-00219

PLoS One. 2021 Dec 29;16(12):e0261528. doi: 10.1371/journal.pone.0261528. eCollection 2021.

ABSTRACT

Multidrug resistance (MDR) has been a significant threat to public health and effective treatment of bacterial infections. Current identification of MDR is primarily based upon the large proportions of isolates resistant to multiple antibiotics simultaneously, and therefore is a belated evaluation. For bacteria with MDR, we expect to see strong correlations in both the quantitative minimum inhibitory concentration (MIC) and the binary susceptibility as classified by the pre-determined breakpoints. Being able to detect correlations from these two perspectives allows us to find multidrug resistant bacteria proactively. In this paper, we provide a Bayesian framework that estimates the resistance level jointly for antibiotics belonging to different classes with a Gaussian mixture model, where the correlation in the latent MIC can be inferred from the Gaussian parameters and the correlation in binary susceptibility can be inferred from the mixing weights. By augmenting the laboratory measurement with the latent MIC variable to account for the censored data, and by adopting the latent class variable to represent the MIC components, our model was shown to be accurate and robust compared with the current assessment of correlations. Applying the model to Salmonella heidelberg samples isolated from human participants in National Antimicrobial Resistance Monitoring System (NARMS) provides us with signs of joint resistance to Amoxicillin-clavulanic acid & Cephalothin and joint resistance to Ampicillin & Cephalothin. Large correlations estimated from our model could serve as a timely tool for early detection of MDR, and hence a signal for clinical intervention.

PMID:34965273 | DOI:10.1371/journal.pone.0261528

PLoS One. 2021 Dec 29;16(12):e0262054. doi: 10.1371/journal.pone.0262054. eCollection 2021.

ABSTRACT

The stress-induced microcrack evolution in rock specimens causes a series of physical changes and heterogeneous deformations. Some of these attributes (such as sound, electricity, heat, etc.) have been effectively used to identify the damage state and precursory information of the rock specimens. However, the strain-field heterogeneity has not been investigated previously. In this study, the relationship of the strain-field heterogeneity and damage evolution of three sandstone specimens under the uniaxial compressive load was analyzed statistically. The acoustic emission (AE) and two-dimensional digital image correlation were employed for real-time evaluation of the AE parameters and strain-field heterogeneity. The results showed that the strain-field heterogeneity was closely related to the rock damage that amplified with the applied stress, and exhibited two features; numerical difference and spatial concentration. Subsequently, these two features were characterized by the two proposed heterogeneous quantitative indicators (i.e., the degree and space heterogeneities). Further, their four transition processes were in agreement with the damage stages confirmed by AE parameters: a relatively constant trend; growth with a relatively constant rate; drastic increase trend; and increase with a high rate to maximum value. Moreover, a time sequence chain for damage precursor was built, where the heterogeneous quantitative indicators and AE parameters differed in sensitivity to microcrack development and can be used as a damage warning at the varying magnitude of the external load.

PMID:34965268 | DOI:10.1371/journal.pone.0262054

PLoS One. 2021 Dec 29;16(12):e0261424. doi: 10.1371/journal.pone.0261424. eCollection 2021.

ABSTRACT

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada’s provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.

PMID:34965272 | DOI:10.1371/journal.pone.0261424

PLoS One. 2021 Dec 29;16(12):e0261625. doi: 10.1371/journal.pone.0261625. eCollection 2021.

ABSTRACT

Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model’s predictive performance. In addition, Cox PH predictive performance was better than RSF.

PMID:34965262 | DOI:10.1371/journal.pone.0261625

Prep Biochem Biotechnol. 2021 Dec 29:1-9. doi: 10.1080/10826068.2021.2004547. Online ahead of print.

ABSTRACT

Solid state fermentation (SSF) simulates the natural conditions fungal growth, where the amount of water in the reaction medium must be restricted, thus limiting the use of liquid substrate. An analytical strategy to deal with this limitation is the design of blending with constraints. Thus, the objective of the work was to optimize two constrained waste mixtures for the production of lipase by Penicillium roqueforti ATCC 10110 under SSF, using different substrates that combine solid and liquid waste. For this, the best fermentation time was determined through a fermentative profile, afterwards a restricted-mix design with lower and upper limits of the components of mixture I (cocoa residue, solid palm oil residue and liquid palm oil residue) and II (cocoa residue, mango residue and palm oil residue liquid palm) was applied. By means of Pareto and contour graphs, the maximum production points of lipase in mixtures I (6.67 ± 0.34 U g^-1) and II (6.87 ± 0.35 U g^-1) were obtained. The restricted mixture design proved to be a promising tool in the production of lipase by P. roqueforti ATCC 10110 under SSF since the use of restrictions is useful when intending to combine solid and liquid residues in fermentation processes.

PMID:34965202 | DOI:10.1080/10826068.2021.2004547

IEEE Trans Vis Comput Graph. 2021 Dec 29;PP. doi: 10.1109/TVCG.2021.3137174. Online ahead of print.

ABSTRACT

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The systems machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinsons Progression Markers Initiative.

PMID:34965212 | DOI:10.1109/TVCG.2021.3137174