Nevin Manimala

Ecol Lett. 2021 May 4. doi: 10.1111/ele.13760. Online ahead of print.

ABSTRACT

Ecological stability refers to a family of concepts used to describe how systems of interacting species vary through time and respond to disturbances. Because observed ecological stability depends on sampling scales and environmental context, it is notoriously difficult to compare measurements across sites and systems. Here, we apply stochastic dynamical systems theory to derive general statistical scaling relationships across time, space, and ecological level of organisation for three fundamental stability aspects: resilience, resistance, and invariance. These relationships can be calibrated using random or representative samples measured at individual scales, and projected to predict average stability at other scales across a wide range of contexts. Moreover deviations between observed vs. extrapolated scaling relationships can reveal information about unobserved heterogeneity across time, space, or species. We anticipate that these methods will be useful for cross-study synthesis of stability data, extrapolating measurements to unobserved scales, and identifying underlying causes and consequences of heterogeneity.

PMID:33945663 | DOI:10.1111/ele.13760

J Gerontol B Psychol Sci Soc Sci. 2021 May 4:gbab079. doi: 10.1093/geronb/gbab079. Online ahead of print.

ABSTRACT

OBJECTIVES: Social relationships are important for older adults’ well-being, including those who live in assisted living (AL) communities. This study explores co-resident networks within an AL community and identifies factors associated with residents’ social ties.

METHODS: Acquaintance and companionship networks within the community are described using cross-sectional survey data (N=38). We use inferential network statistical methods to estimate parameters for factors associated with residents’ acquaintance and companionship ties.

RESULTS: Residents reported an average of 10 acquaintances and almost four companionships with other residents in the sample. The likelihood a resident had an acquaintance was associated with higher levels of cognitive functioning (p<.05), higher levels of physical limitations (p<.01), living in the AL community for a longer time (p<.01), and less frequent contact with outside family and friends (p<.05). Acquaintances were more likely between residents who moved in around the same time as each other (p<.01), lived on the same floor (p<.001), or had similar levels of physical limitations (p<.05). Companionships were more likely to be reported by male residents (p<.05) and residents with higher levels of cognitive functioning (p<.05) or depressive symptoms (p<.05). Longtime residents were more popular as companions (p<.01). Companionships were more likely between residents who lived on the same floor (p<.001) or were similar in age (p<.01).

DISCUSSION: This research contributes to the literature of older adults’ non-kin social relationships by providing detailed descriptions of the acquaintance and companionship networks within an AL community, quantifying correlates of residents’ social ties, and distinguishing between acquaintances and companions.

PMID:33945609 | DOI:10.1093/geronb/gbab079

PLoS One. 2021 May 4;16(5):e0251152. doi: 10.1371/journal.pone.0251152. eCollection 2021.

ABSTRACT

PURPOSE: To compare the biometry and prediction of postoperative refractive outcomes of four different formulae (Haigis, SRK/T, Holladay1, Barrett Universal II) obtained by swept-source optical coherence tomography (SS-OCT) biometers and partial coherence interferometry (PCI; IOLMaster ver 5.4).

METHODS: We compared the biometric values of SS-OCT (ANTERION, Heidelberg Engineering Inc., Heidelberg, Germany) and PCI (IOLMaster, Carl Zeiss Meditec, Jena, Germany). Predictive errors calculated using four different formulae (Haigis, SRKT, Holladay1, Barrett Universal II) were compared at 1 month after cataract surgery.

RESULTS: The mean preoperative axial length (AL) showed no statistically significant difference between SS-OCT and PCI (SS-OCT: 23.78 ± 0.12 mm and PCI: 23.77 ± 0.12 mm). The mean anterior chamber depth (ACD) was 3.30 ± 0.04 mm for SS-OCT and 3.23 ± 0.04 mm for PCI, which was significantly different between the two techniques. The mean corneal curvature also differed significantly between the two techniques. The difference in mean arithmetic prediction error was significant in the Haigis, SRKT, and Holladay1 formulae. The difference in mean absolute prediction error was significant in all four formulae.

CONCLUSIONS: SS-OCT and PCI demonstrated good agreement on biometric measurements; however, there were significant differences in some biometric values. These differences in some ocular biometrics can cause a difference in refractive error after cataract surgery. New type SS-OCT was not superior to the IOL power prediction calculated by PCI.

PMID:33945581 | DOI:10.1371/journal.pone.0251152

PLoS One. 2021 May 4;16(5):e0250985. doi: 10.1371/journal.pone.0250985. eCollection 2021.

ABSTRACT

BACKGROUND: Sleep is a physiologic necessity for all humankind. Pregnant women, in particular, need adequate sleep to develop their fetuses as well as save energy required for delivery. A change in sleep quality and quantity is the most common phenomena during pregnancy due to mechanical and hormonal factors. However, there is a scarcity of data about poor sleep quality and its associated factors among pregnant mothers in Ethiopia. Therefore, this study aims to determine the prevalence of poor sleep quality and its associated factors among pregnant mothers at Wadila primary hospital, Ethiopia.

METHODS: Institution based cross-sectional study design was employed on 411 pregnant mothers. Data were collected using a pre-tested interviewer administered questionnaire. SPSS Version 23 for Windows software was used for data analyses. Bivariate analysis was conducted to detect the association between dependent and independent variables, and to choose candidate variables (p < 0.25) for multivariate logistic regression. Statistical significance was set at p-value <0.05.

RESULTS: A total of 411 participants were included in the study making a response rate of 97.4%. Overall, 68.4% of participants found to have poor sleep quality (PSQI>5). Age of the mother [age 20-30 years; AOR = 4.3 CI (1.8, 9.9), p = 0.001, and age >30 years; AOR = 4.7 CI (1.6, 13.9) p = 0.005], gestational age [second trimester, AOR = 2.46, CI (1.2, 4.9), p = 0.01 and third trimester, AOR = 7.5, CI (3.2, 17.8), p = 0.000] and parity [multiparous women; AOR = 2.1(1.24, 3.6) p = 0.006] were predictor variables for poor sleep quality among pregnant mothers.

CONCLUSION: More than two-third of pregnant mothers had poor sleep quality. Advanced maternal age, increased gestational age and multiparty are found to be predictors of poor sleep quality in pregnant women.

PMID:33945578 | DOI:10.1371/journal.pone.0250985

PLoS One. 2021 May 4;16(5):e0250842. doi: 10.1371/journal.pone.0250842. eCollection 2021.

ABSTRACT

BACKGROUND: Occupational stress is associated with adverse outcomes for medical professionals and patients. In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure.

METHODS: We applied four machine learning classifiers (random forest, support vector machine, K-nearest neighbors’, and artificial neural network) and logistic regression as standard approach to analyze factors contributing to chronic stress in practice assistants. Chronic stress had been measured by the standardized, self-administered TICS-SSCS questionnaire. The performance of these models was compared in terms of predictive accuracy based on the ‘operating area under the curve’ (AUC), sensitivity, and positive predictive value.

FINDINGS: Compared to the standard logistic regression model (AUC 0.636, 95% CI 0.490-0.674), all machine learning models improved prediction: random forest +20.8% (AUC 0.844, 95% CI 0.684-0.843), artificial neural network +12.4% (AUC 0.760, 95% CI 0.605-0.777), support vector machine +15.1% (AUC 0.787, 95% CI 0.634-0.802), and K-nearest neighbours +7.1% (AUC 0.707, 95% CI 0.556-0.735). As best prediction model, random forest showed a sensitivity of 99% and a positive predictive value of 79%. Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders.

CONCLUSIONS: Regarding chronic stress prediction, machine learning classifiers, especially random forest, provided more accurate prediction compared to classical logistic regression. Interventions to reduce chronic stress in practice personnel should primarily address the identified workplace characteristics.

PMID:33945572 | DOI:10.1371/journal.pone.0250842

PLoS One. 2021 May 4;16(5):e0250993. doi: 10.1371/journal.pone.0250993. eCollection 2021.

ABSTRACT

BACKGROUND: Suboptimal patient adherence to pharmacological therapy of type 2 diabetes may be due in part to pill burden. One way to reduce pill burden in patients who need multiple medications is to use fixed-dose combinations. Our study aimed to compare the effects of fixed-dose combination versus loose-dose combination therapy on medication adherence and persistence, health care utilization, therapeutic safety, morbidities, and treatment modification in patients with type 2 diabetes over three years.

METHODS: Using administrative data, we conducted a retrospective controlled cohort study comparing type 2 diabetes patients who switched from monotherapy to either a fixed-dose combination or a loose-dose combination. Adherence was assessed as the primary endpoint and calculated as the proportion of days covered with medication. After using entropy balancing to eliminate differences in observable baseline characteristics between the two groups, we applied difference-in-difference estimators for each outcome to account for time-invariant unobservable heterogeneity.

RESULTS: Of the 990 type 2 diabetes patients included in our analysis, 756 were taking a fixed-dose combination and 234 were taking a loose-dose combination. We observed a statistically significantly higher change in adherence (year one: 0.22, p<0.001, year two: 0.25, p<0.001, and year three: 0.29, p<0.001) as well as higher persistence and a smaller change in the number of drug prescriptions in each of the three years in the fixed-dose combination group compared to the loose-dose combination group. The differences were most pronounced in patients who were poorly adherent, had a high pill burden, or did not have a severe concomitant disease.

CONCLUSION: Our results indicate that taking a fixed-dose combination can lead to a significant improvement in adherence to pharmacological therapy of type 2 diabetes compared to a loose-dose combination. In particular, these findings suggest that reducing pill burden may improve disease management among patients with more complex medication demand and patients who have demonstrated poor medication adherence.

PMID:33945556 | DOI:10.1371/journal.pone.0250993

PLoS Comput Biol. 2021 May 4;17(5):e1008976. doi: 10.1371/journal.pcbi.1008976. Online ahead of print.

ABSTRACT

Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package “MetaGSCA”. It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.

PMID:33945541 | DOI:10.1371/journal.pcbi.1008976

J Am Acad Orthop Surg Glob Res Rev. 2021 May 4;5(5):e21.00073. doi: 10.5435/JAAOSGlobal-D-21-00073.

ABSTRACT

INTRODUCTION: Within the geriatric hip fracture population, there exists a subset of patients whose length of inpatient hospital stay is excessive relative to the average. A better understanding of the risk factors associated with this group would be of value so that targeted prevention efforts can be properly directed. The goal of this study was to identify and characterize the risk factors associated with an extended length of hospital stay (eLOS) in the geriatric hip fracture population. In addition, a statistical model was created to predict the probability of eLOS in a geriatric hip fracture patient.

METHODS: The National Surgical Quality Improvement Program database (2005 to 2018) was searched for patients aged ≥65 years who underwent hip fracture surgery. Patients with a hospital stay greater than or equal to 14 days were considered to have an eLOS. A multivariate logistic regression model using 24 patient characteristics from two-thirds of the study population was created to determine independent risk factors predictive of having an eLOS; the remaining one-third of the population was used for internal model validation. Regression analyses were performed to determine preoperative and postoperative risk factors for having an eLOS.

RESULTS: A total of 77,144 patients were included in the study. Preoperatively, male sex, dyspnea, ventilator use, chronic obstructive pulmonary disease, American Society of Anesthesiologist class 3 and 4, and increased admission-to-operation time were among the factors associated with higher odds of having an eLOS (all P < 0.001). Postoperatively, patients with acute renal failure had the highest likelihood of eLOS (odds ratio [OR] 7.664), followed by ventilator use >48 hours (OR 4.784) and pneumonia (OR 4.332).

DISCUSSION: Among geriatric hip fracture patients, particular efforts should be directed toward optimizing those with preoperative risk factors for eLOS. Preemptive measures to target the postoperative complications with the strongest eLOS association may be beneficial for both the patient and the healthcare system as a whole.

PMID:33945514 | DOI:10.5435/JAAOSGlobal-D-21-00073

BMJ Open. 2021 Apr 8;11(4):e043436. doi: 10.1136/bmjopen-2020-043436.

ABSTRACT

OBJECTIVES: To summarise current evidence on the use of pentoxifylline (PTX) to prevent contrast-induced nephropathy (CIN).

METHODS: The PubMed, Embase and CENTRAL databases were searched for randomised controlled trials including patients with and without PTX undergoing contrast media exposure. We analysed the incidence of CIN and serum creatinine changes before and after contrast media exposure. All statistical analyses were conducted with Review Manager V.5.3.

RESULTS: We finally enrolled in seven randomised controlled trials with a total of 1484 patients in this analysis. All of seven included studies were performed in patients undergoing angioplasty or stenting. The overall rates of CIN were 8.8% and 10.4% in the PTX groups and control groups, respectively. However, no significant reduction in the CIN rate was observed in the patients treated with PTX compared with the control groups (OR 0.81, 95% CI 0.57 to 1.13, I²=0, p=0.21). All studies reported no hospital mortality and the new requirement for dialysis during the trials.

CONCLUSION: Perioperative administration of PTX to patients undergoing angioplasty did not significantly reduce the development of CIN but showed some weak tendency of lower serum creatinine increase. Based on the available trials, the evidence does not support the administration of PTX for the prevention of CIN. More trials with larger sample sizes are needed to evaluate the role of PTX in CIN prevention.

PMID:33945499 | DOI:10.1136/bmjopen-2020-043436

IEEE Trans Image Process. 2021 May 4;PP. doi: 10.1109/TIP.2021.3076310. Online ahead of print.

ABSTRACT

Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial. Though U-shaped encoder-decoder frameworks have been witnessed to be successful, most of them share a common drawback of mask unawareness in feature extraction because all convolution windows (or regions), including those with various shapes of missing pixels, are treated equally and filtered with fixed learned kernels. To this end, we propose our novel mask-aware inpainting solution. Firstly, a Mask-Aware Dynamic Filtering (MADF) module is designed to effectively learn multi-scale features for missing regions in the encoding phase. Specifically, filters for each convolution window are generated from features of the corresponding region of the mask. The second fold of mask awareness is achieved by adopting Point-wise Normalization (PN) in our decoding phase, considering that statistical natures of features at masked points differentiate from those of unmasked points. The proposed PN can tackle this issue by dynamically assigning point-wise scaling factor and bias. Lastly, our model is designed to be an end-to-end cascaded refinement one. Supervision information such as reconstruction loss, perceptual loss and total variation loss is incrementally leveraged to boost the inpainting results from coarse to fine. Effectiveness of the proposed framework is validated both quantitatively and qualitatively via extensive experiments on three public datasets including Places2, CelebA and Paris StreetView.

PMID:33945479 | DOI:10.1109/TIP.2021.3076310