Nevin Manimala

BMC Bioinformatics. 2022 Jun 1;23(1):208. doi: 10.1186/s12859-022-04740-9.

ABSTRACT

BACKGROUND: Bioinformatics investigators often gain insights by combining information across multiple and disparate data sets. Merging data from multiple sources frequently results in data sets that are incomplete or contain missing values. Although missing data are ubiquitous, existing implementations of Gaussian mixture models (GMMs) either cannot accommodate missing data, or do so by imposing simplifying assumptions that limit the applicability of the model. In the presence of missing data, a standard ad hoc practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates.

RESULTS: Here we present missingness-aware Gaussian mixture models (MGMM), an R package for fitting GMMs in the presence of missing data. Unlike existing GMM implementations that can accommodate missing data, MGMM places no restrictions on the form of the covariance matrix. Using three case studies on real and simulated ‘omics data sets, we demonstrate that, when the underlying data distribution is near-to a GMM, MGMM is more effective at recovering the true cluster assignments than either the existing GMM implementations that accommodate missing data, or fitting a standard GMM after state of the art imputation. Moreover, MGMM provides an accurate assessment of cluster assignment uncertainty, even when the generative distribution is not a GMM.

CONCLUSION: Compared to state-of-the-art competitors, MGMM demonstrates a better ability to recover the true cluster assignments for a wide variety of data sets and a large range of missingness rates. MGMM provides the bioinformatics community with a powerful, easy-to-use, and statistically sound tool for performing clustering and density estimation in the presence of missing data. MGMM is publicly available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM .

PMID:35650523 | DOI:10.1186/s12859-022-04740-9

J Gen Intern Med. 2022 Jun 1. doi: 10.1007/s11606-022-07674-3. Online ahead of print.

ABSTRACT

BACKGROUND: Communication with clinicians is an important component of a hospitalized patient’s experience.

OBJECTIVE: To test the impact of standardized hospitalist information cards on the patient experience.

DESIGN: Quasi-experimental study in a U.S. tertiary-care center.

PARTICIPANTS: All-comer medicine inpatients.

INTERVENTIONS: Standardized hospitalist information cards containing name and information on a hospitalist’s role and availability vs. usual care.

MAIN MEASURES: Patients’ rating of the overall communication as excellent (“top-box” score); qualitative feedback summarized via inductive coding.

KEY RESULTS: Five hundred sixty-six surveys from 418 patients were collected for analysis. In a multivariate regression model, standardized hospitalist information cards significantly improved the odds of a “top-box” score on overall communication (odds ratio: 2.32; 95% confidence intervals: 1.07-5.06). Other statistically significant covariates were patient age (0.98, 0.97-0.99), hospitalist role (physician vs. advanced practice provider, 0.56; 0.38-0.81), and hospitalist-patient gender combination (female-female vs. male-male, 2.14; 1.35-3.40). Eighty-seven percent of patients found the standardized hospitalist information cards useful, the perceived most useful information being how to contact the hospitalist and knowing their schedule.

CONCLUSIONS: Hospitalized patients’ experience of their communication with hospitalists may be improved by using standardized hospitalist information cards. Younger patients cared for by a team with an advanced practice provider, as well as female patients paired with female providers, were more likely to be satisfied with the overall communication. Assessing the impact of information cards should be studied in other settings to confirm generalizability.

PMID:35650470 | DOI:10.1007/s11606-022-07674-3

Psychon Bull Rev. 2022 Jun 1. doi: 10.3758/s13423-022-02075-3. Online ahead of print.

ABSTRACT

How good are people’s statistical intuitions? Recent research has highlighted that sequential experience of statistical information improves adults’ statistical intuitions relative to situations where this information is described. Yet little is known about whether this is also the case for children’s statistical intuitions. In a study with 100 children (8-11 years old) and 100 adults (19-35 years old), we found that sequentially experiencing statistical information improved both adults’ and children’s inferences in two paradigmatic reasoning problems: conjunction and Bayesian reasoning problems. Moreover, adults’ statistical competencies when they learned statistical information through description were surpassed by children’s inferences when they learned through experience. We conclude that experience of statistical information plays a key role in shaping children’s reasoning under uncertainty-a conclusion that has important implications for education policy.

PMID:35650464 | DOI:10.3758/s13423-022-02075-3

Biotechnol Lett. 2022 Jun 1. doi: 10.1007/s10529-022-03258-7. Online ahead of print.

ABSTRACT

OBJECTIVES: Hydrodynamics, mixing and shear are terms often used when explaining or modelling scale differences, but other scale differences, such as evaporation, can arise from non-hydrodynamic factors that can be managed with some awareness and effort.

RESULTS: We present an engineering approach to the prediction of evaporation rates in bioreactors based on gH₂O/Nm³ of air entering and leaving the bioreactor and confirm its usefulness in a 28-run design of experiments investigating the effects of aeration rate (0.02 to 2.0 VVM), condenser temperature (10 to 20 °C), fill (2.5 to 5 kg), broth temperature (25 to 40 °C) and agitator speed (25 to 800 rpm). Aeration rate and condenser temperature used in the engineering prediction provided a practically useful estimate of evaporation; the other factors, while statistically identified as having some influence, were of negligible practical usefulness. Evaporation rates were never found to be zero, and could be at least 10% different to those expected at scale.

CONCLUSIONS: An assessment of evaporation rates for any project is encouraged, and it is recommended that the effects are accounted for by measurements, modelling or by tuning the exhaust cooling device temperature to minimize scale differences.

PMID:35650455 | DOI:10.1007/s10529-022-03258-7

Nat Protoc. 2022 Jun 1. doi: 10.1038/s41596-022-00696-5. Online ahead of print.

ABSTRACT

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus ‘healthy’ control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.

PMID:35650452 | DOI:10.1038/s41596-022-00696-5

Semin Immunopathol. 2022 Jun 1. doi: 10.1007/s00281-022-00950-8. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWAS) for autoimmune hepatitis (AIH) and GWAS/genome-wide meta-analyses (GWMA) for primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) have been successful over the past decade, identifying about 100 susceptibility loci in the human genome, with strong associations with the HLA locus and many susceptibility variants outside the HLA locus with relatively low risk. However, identifying causative variants and genes and determining their effects on liver cells and their immunological microenvironment is far from trivial. Polygenic risk scores (PRSs) based on current genome-wide data have limited potential to predict individual disease risk. Interestingly, results of mediated expression score regression analysis provide evidence that a substantial portion of gene expression at susceptibility loci is mediated by genetic risk variants, in contrast to many other complex diseases. Genome- and transcriptome-wide comparisons between AIH, PBC, and PSC could help to better delineate the shared inherited component of autoimmune liver diseases (AILDs), and statistical fine-mapping, chromosome X-wide association testing, and genome-wide in silico drug screening approaches recently applied to GWMA data from PBC could potentially be successfully applied to AIH and PSC. Initial successes through single-cell RNA sequencing (scRNA-seq) experiments in PBC and PSC now raise high hopes for understanding the impact of genetic risk variants in the context of liver-resident immune cells and liver cell subpopulations, and for bridging the gap between genetics and disease.

PMID:35650446 | DOI:10.1007/s00281-022-00950-8

Appl Physiol Nutr Metab. 2022 Jun 1. doi: 10.1139/apnm-2021-0618. Online ahead of print.

ABSTRACT

This study investigated intakes of total, n-3, and n-6 polyunsaturated fatty acids (PUFA) in 109 preschool-aged children who participated in the Guelph Family Health Study pilot. Intakes of total, n-3, and n-6 PUFA did not meet recommendations. This study highlights the need for additional monitoring and potential interventions to improve PUFA intake in preschool-aged children. Clinical trial #NCT02223234. Novelty: Canadian preschool-aged children are not consuming enough n-3 and n-6 polyunsaturated fatty acids.

PMID:35649282 | DOI:10.1139/apnm-2021-0618

Int J Prosthodont. 2022 May 25. doi: 10.11607/ijp.7457. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the risk factors related to failure of removable partial dentures (RPDs) and to compare the survival of RPDs when abutment teeth have good vs reduced bone support.

MATERIALS AND METHODS: In this longitudinal patient record-based cohort study with a mean follow-up of 44.9 months, Cox regression models were used to evaluate the associations between clinical covariates and abutment tooth failure, as well as prosthesis failure.

RESULTS: A total of 142 patients and 172 RPDs were included. Of the 172 prostheses, 64 (nearly 40%) failed after a mean time of about 4 years. Loss of abutment teeth was the main reason leading to RPD failure, followed closely by poor fit and adaptation. No factor was shown to be statistically significant at the prosthesis level. Women (Hazard Ratio [HR] = 0.542), endodontic treatment (HR = 3.460), presence of post and core (HR = 0.302), presence of a prosthetic crown (HR = 3.403), and abutment tooth type (in relation to incisor: canine HR = 0.196, premolar HR = 0.449) were the risk factors statistically significantly associated with the loss of abutment teeth. The pre-prosthesis amount of bone support of the teeth did not affect their prognosis as abutments for RPDs.

CONCLUSION: RPD treatment modality presented a high failure rate after a mean follow-up of 4 years. Vital abutment teeth had a better survival rate than the ones treated endodontically (whether restored with or without a post and core); nevertheless, the amount of bone support did not affect their survival.

PMID:35649280 | DOI:10.11607/ijp.7457

J Phys Chem Lett. 2022 Jun 1:5009-5016. doi: 10.1021/acs.jpclett.2c00685. Online ahead of print.

ABSTRACT

Atomic descriptions of peptide aggregation nucleation remain lacking due to the difficulty of exploring complex configurational spaces on long time scales. To elucidate this process, we develop a multiscale approach combining a metadynamics-based method with cluster statistical mechanics to derive concentration-dependent free energy surfaces of nucleation at near-atomic resolution. A kinetic transition network of nucleation is then constructed and employed to systematically explore nucleation pathways and kinetics through stochastic simulations. This approach is applied to describe Aβ_16-21 amyloid nucleation, revealing a two-step mechanism involving disordered aggregates at millimolar concentration, and an unexpected mechanism at submillimolar concentrations that exhibits kinetics reminiscent of classical nucleation but atypical pathways involving growing clusters with structured cores wrapped by disordered surface. When this atypical mechanism is operative, critical nucleus size can be reflected by the nucleation reaction order. Collectively, our approach paves the way for a more quantitative and detailed understanding of peptide aggregation nucleation.

PMID:35649244 | DOI:10.1021/acs.jpclett.2c00685

Ann Am Thorac Soc. 2022 Jun 1. doi: 10.1513/AnnalsATS.202106-651OC. Online ahead of print.

ABSTRACT

RATIONALE: There has been a paradigm shift to partner with family caregivers by actively involving them in the direct care of the patient throughout the critical illness trajectory. Before effectively engaging family members in patient care, clinicians must assess characteristics and circumstances that may affect caregiver readiness to assume a caregiving role in the ICU.

OBJECTIVE: To determine how demographic, clinical, and psychological factors are related to characteristics of family caregiver readiness to engage in ICU patient care.

METHODS: A convenience sample of ICU family caregivers of both adult and pediatric ICU patients was recruited for this cross-sectional study. Participants completed the following measures: PROMIS-29; Hospital Anxiety and Depression Scale; Caregiver Self-Efficacy Scale; Preparedness for Caregiving Scale; Patient Activation Measure for Caregivers; and Family Caregiver’s Motives for Helping Scale. Data were collected via self-report at a single time point while the caregiver was visiting the critically ill patient in the ICU. Data analysis consisted of descriptive statistics and bivariate correlations.

RESULTS: Caregivers (N=127) were primarily White (82.7%), females (77.2%), with a mean age of 51.8 (SD=15.6). Most were either spouses (37.8%) or parents (32.3%) of the ICU patient. Patients were primarily adult (76.4%) with a mean APACHE III of 45.9 (SD=22.5). There were significant (p<0.05) negative correlations between: depression, anxiety, and fatigue and all subscales of self-efficacy (Resilience r = -.18 to -.30; Self-Maintenance r = -.44 to -.63; Emotional Connectivity r = -.27 to -.41; Instrumental Giving r = -.34 to -.46) Caregiver depression was negatively correlated with caregiver activation (r = -.199) and caregiver preparedness (r = -.300). Social satisfaction was positively correlated (p<.05) with caregiving preparedness, motivation, and all subscales of self-efficacy (Preparedness r = .19; Motivation = .24; Resilience r = .21; Self-Maintenance r = .49; Emotional Connectivity r = .29; Instrumental Giving r = .36).

CONCLUSIONS: We found that caregiver symptoms of depression, anxiety and fatigue are inversely related to caregiver preparation, motivation, and self-efficacy. In order to develop effective interventions for ICU family caregivers, further research is needed to understand the relationship between caregiver well-being, caregiving readiness, and caregiver involvement in patient care.

PMID:35649201 | DOI:10.1513/AnnalsATS.202106-651OC