On standardization of controls in lifespan studies

The search for interventions to slow down and even reverse aging is a burgeoning field. The literature cites hundreds of supposedly beneficial pharmacological and genetic interventions in model organisms: mice, rats, flies and worms, where research into physiology is routinely accompanied by lifespan data. However, when experimental animals from one article live as long as controls from another article, comparing the results of interventions across studies can yield misleading outcomes. Theoretically, all lifespan data are ripe for re-analysis: we could contrast the molecular targets and pathways across studies and help focus the further search for interventions. Alas, the results of most longevity studies are difficult to compare. This is in part because there are no clear, universally accepted standards for conducting such experiments or even for reporting such data. The situation is worsened by the fact that the authors often do not describe experimental conditions completely. As a result, works on longevity make up a set of precedents, each of which might be interesting in its own right, yet incoherent and incomparable at least for the reason that in a general context, it may indicate, for example, not prolonging the life of an average organism, but compensating for any genetic abnormalities of a particular sample or inappropriate living conditions. Here we point out specific issues and propose solutions for quality control by checking both inter- and intra-study consistency of lifespan data.

Habitat fragmentation reduces survival and drives source-sink dynamics for a large carnivore

Rigorous understanding of how environmental conditions impact population dynamics is essential for species conservation, especially in mixed-use landscapes where source-sink dynamics may be at play. Conservation of large carnivore populations in fragmented, human-dominated landscapes is critical for their long-term persistence. However, living in human-dominated landscapes comes with myriad costs, including direct anthropogenic mortality and sublethal energetic costs. How these costs impact individual fitness and population dynamics are not fully understood, partly due to the difficulty in collecting long-term demographic data for these species. Here, we analyzed an 11-year dataset on puma (Puma concolor) space use, mortality, and reproduction in the Santa Cruz Mountains, California, USA, to quantify how living in a fragmented landscape impacts individual survival and population dynamics. Long-term exposure to housing density drove mortality risk for female pumas, resulting in an 18-percentage-point reduction in annual survival for females in exurban versus remote areas. While the overall population growth rate appeared stable, reduced female survival in more developed areas resulted in source-sink dynamics across the study area, with 42.1% of the Santa Cruz Mountains exhibiting estimated population growth rates <1. Since habitat selection is often used as a proxy for habitat quality, we also assessed whether puma habitat selection predicted source and sink areas. Patterns of daytime puma habitat selection predicted source areas, while time-of-day-independent habitat selection performed less well as a proxy. These results illuminate the individual- and population-level consequences of habitat fragmentation for large carnivores, illustrating that habitat fragmentation can produce source-sink dynamics that may not be apparent from other metrics of habitat quality. Locally, conserving high-quality source habitat within the Santa Cruz Mountains is necessary to support long-term puma population persistence. More broadly, source-sink dynamics may at play for other carnivore populations in similar fragmented systems, and linking landscape conditions to population dynamics is essential for effective conservation. Caution should be used in inferring habitat quality from habitat selection alone, but these results shed light on metrics of selection that may be better or worse proxies to identify source areas for large carnivores.

Survival and recovery modeling of acute kidney injury in critically ill adults

Objectives:

Acute kidney injury is common among the critically ill. However, the incidence, medication use, and outcomes of acute kidney injury have been variably described. We conducted a single-center, retrospective cohort study to examine the risk factors and correlates associated with acute kidney injury in critically ill adults with a particular focus on medication class usage.

Methods:

We reviewed the electronic medical records of all adult patients admitted to an intensive care unit between 1 February and 30 August 2020. Acute kidney injury was defined by the 2012 Kidney Disease: Improving Global Outcomes guidelines. Data included were demographics, comorbidities, symptoms, laboratory parameters, interventions, and outcomes. The primary outcome was acute kidney injury incidence. A Least Absolute Shrinkage and Selection Operator regression model was used to determine risk factors associated with acute kidney injury. Secondary outcomes including acute kidney injury recovery and intensive care unit mortality were analyzed using a Cox regression model.

Results:

Among 226 admitted patients, 108 (47.8%) experienced acute kidney injury. 37 (34.3%), 39 (36.1%), and 32 patients (29.6%) were classified as acute kidney injury stages I–III, respectively. Among the recovery and mortality cohorts, analgesics/sedatives, anti-infectives, and intravenous fluids were significant (p-value < 0.05). The medication classes IV-fluid electrolytes nutrition (96.7%), gastrointestinal (90.2%), and anti-infectives (81.5%) were associated with an increased odds of developing acute kidney injury, odd ratios: 1.27, 1.71, and 1.70, respectively. Cox regression analyses revealed a significantly increased time-varying mortality risk for acute kidney injury-stage III, hazard ratio: 4.72 (95% confidence interval: 1–22.33). In the recovery cohort, time to acute kidney injury recovery was significantly faster in stage I, hazard ratio: 9.14 (95% confidence interval: 2.14–39.06) cohort when compared to the stage III cohort.

Conclusion:

Evaluation of vital signs, laboratory, and medication use data may be useful to determine acute kidney injury risk stratification. The influence of particular medication classes further impacts the risk of developing acute kidney injury, necessitating the importance of examining pharmacotherapeutic regimens for early recognition of renal impairment and prevention.

Metavariables Resuming Host Immune Features and Nodal Involvement Are Associated with Oncological Outcomes in Oral Cavity Squamous Cell Carcinoma

Oral cavity squamous cell carcinoma (OSCC) is a common head and neck cancer characterized by a poor prognosis associated with locoregional or distant failure. Among the predictors of prognosis, a dense infiltration of adaptive immune cells is protective and associated with improved clinical outcomes. However, few tools are available to integrate immune contexture variables into clinical settings. By using digital microscopy analysis of a large retrospective OSCC cohort (n = 182), we explored the clinical significance of tumor-infiltrating CD8+ T-cells. To this end, CD8+ T-cells counts were combined with well-established clinical variables and peripheral blood immune cell parameters. Through variable clustering, five metavariables (MV) were obtained and included descriptors of nodal (NODALMV) and primary tumor (TUMORMV) involvement, the frequency of myeloid (MYELOIDMV) or lymphoid (LYMPHOIDMV) peripheral blood immune cell populations, and the density of tumor-infiltrating CD8+ T-cells (TI-CD8MV). The clinical relevance of the MV was evaluated in the multivariable survival models. The NODALMV was significantly associated with all tested outcomes (p < 0.001), the LYMPHOIDMV showed a significant association with the overall, disease-specific and distant recurrence-free survival (p < 0.05) and the MYELOIDMV with the locoregional control only (p < 0.001). Finally, TI-CD8MV was associated with distant recurrence-free survival (p = 0.029). Notably, the performance in terms of survival prediction of the combined effect of NODALMV and immune metavariables (LYMPHOIDMV, MYELOIDMV and TI-CD8MV) was superior to the TNM stage for most of the outcomes analyzed. These findings indicate that the analysis of the baseline host immune features are promising tools to complement clinical features, in stratifying the risk of recurrences.

No effect of passive integrated transponder tagging method on survival or body condition in a northern population of Black-capped Chickadees (Poecile atricapillus)

Passive integrated transponder (PIT) tags allow a range of individual-level data to be collected passively and have become a commonly used technology in many avian studies. Although the potential adverse effects of PIT tags have been evaluated in several species, explicit investigations of their impacts on small (<12 g) birds are limited. This is important, because it is reasonable to expect that smaller birds could be impacted more strongly by application of PIT tags. In this study, we individually marked Black-capped Chickadees (Poecile atricapillus), a small (circa 10 g) passerine, at the University of Alberta Botanic Garden to evaluate potential lethal and sublethal effects of two PIT tagging methods: attachment to leg bands or subcutaneous implantation. We used a Cox proportional hazards model to compare the apparent survival of chickadees with leg band (N = 79) and implanted PIT tags (N = 77) compared with control birds that received no PIT tags (N = 76) over the subsequent 2 years based on mist net recaptures. We used radio-frequency identification (RFID) redetections of leg band PIT tags to evaluate sex-specific survival and increase the accuracy of our survival estimates. We also used a generalized linear regression model to compare the body condition of birds recaptured after overwintering with leg band PIT tags, implanted PIT tags, or neither. Our analysis found no evidence for adverse effects of either PIT tagging method on survival or body condition. While we recommend carefully monitoring study animals and evaluating the efficacy of different PIT tagging methods, we have shown that both leg band and subcutaneously implanted PIT tags ethical means of obtaining individualized information in a small passerine.

Correlation-adjusted regression survival scores for high-dimensional variable selection

BACKGROUND

The development of classification methods for personalized medicine is highly dependent on the identification of predictive genetic markers. In survival analysis, it is often necessary to discriminate between influential and noninfluential markers. It is common to perform univariate screening using Cox scores, which quantify the associations between survival and each of the markers to provide a ranking. Since Cox scores do not account for dependencies between the markers, their use is suboptimal in the presence of highly correlated markers.

METHODS

As an alternative to the Cox score, we propose the correlation-adjusted regression survival (CARS) score for right-censored survival outcomes. By removing the correlations between the markers, the CARS score quantifies the associations between the outcome and the set of "decorrelated" marker values. Estimation of the scores is based on inverse probability weighting, which is applied to log-transformed event times. For high-dimensional data, estimation is based on shrinkage techniques.

RESULTS

The consistency of the CARS score is proven under mild regularity conditions. In simulations with high correlations, survival models based on CARS score rankings achieved higher areas under the precision-recall curve than competing methods. Two example applications on prostate and breast cancer confirmed these results. CARS scores are implemented in the R package carSurv.

CONCLUSIONS

In research applications involving high-dimensional genetic data, the use of CARS scores for marker selection is a favorable alternative to Cox scores even when correlations between covariates are low. Having a straightforward interpretation and low computational requirements, CARS scores are an easy-to-use screening tool in personalized medicine research.

A transformation class for spatio-temporal survival data with a cure fraction

We propose a hierarchical Bayesian methodology to model spatially or spatio-temporal clustered survival data with possibility of cure. A flexible continuous transformation class of survival curves indexed by a single parameter is used. This transformation model is a larger class of models containing two special cases of the well-known existing models: the proportional hazard and the proportional odds models. The survival curve is modeled as a function of a baseline cumulative distribution function, cure rates, and spatio-temporal frailties. The cure rates are modeled through a covariate link specification and the spatial frailties are specified using a conditionally autoregressive model with time-varying parameters resulting in a spatio-temporal formulation. The likelihood function is formulated assuming that the single parameter controlling the transformation is unknown and full conditional distributions are derived. A model with a non-parametric baseline cumulative distribution function is implemented and a Markov chain Monte Carlo algorithm is specified to obtain the usual posterior estimates, smoothed by regional level maps of spatio-temporal frailties and cure rates. Finally, we apply our methodology to melanoma cancer survival times for patients diagnosed in the state of New Jersey between 2000 and 2007, and with follow-up time until 2007.

Predictors of death and production performance of layer chickens in opened and sealed pens in a tropical savannah environment

BACKGROUND

Layer chickens are exposed to high risks of production losses and mortality with impact on farm profitability. The harsh tropical climate and severe disease outbreaks, poor biosecurity, sub-minimal vaccination and treatment protocols, poor management practices, poor chick quality, feed-associated causes, and unintended accidents oftentimes aggravate mortality and negatively affect egg production. The objectives of this study were to estimate the probability of survival and evaluate risk factors for death under different intensive housing conditions in a tropical climate, and to assess the production performance in the housing systems.

RESULTS

Daily mean mortality percentages and egg production figures were significantly lower and higher in the sealed pens and open houses (P < 0. 001) respectively. The total mean feed consumption/bird/day was similar for the open sided and sealed pens but the mean feed quantity per egg produce was significantly lower in the sealed pens ((P < 0.005). Seasons differently impacted on mortality with the hot-dry season producing significantly higher risk of mortality (61 times) and reduced egg production. Other parameters also differed except the egg production during the cold-dry season. Layers in sealed pens appear to have higher probability of survival and the Kaplan-Meir survival curves differed for each pen; ≥ 78 weeks old layer have higher probability of survival compared with the younger chickens and the 19-38 weeks age category are at highest risk of death (P < 0.001). The hazard-ratio for mortality of layers raised in sealed pens was 0.568 (56.8%).

CONCLUSION

Reasons for spiked mortality in layer chickens may not always be associated with disease. Hot-dry climatic environment is associated with heat stress, waning immunity and inefficient feed usage and increase probability of death with reduced egg production; usage of environmentally controlled building in conditions where environmental temperature may rise significantly above 25°C will reduce this impact. Since younger birds (19-38 weeks) are at higher risk of death due to stress of coming into production, management changes and diseases, critical implementation of protocols that will reduce death at this precarious period becomes mandatory. Whether older chickens' better protection from death is associated with many prophylactic and metaphylactic regimen of medications/vaccination will need further investigation.

Feature selection and survival modeling in The Cancer Genome Atlas

Purpose

Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling.

Patients and methods

The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1) 1-nearest neighbor (1-NN) survival prediction method; (2) random patient selection method and a Cox-based regression method with nested cross-validation; (3) least absolute shrinkage and selection operator (LASSO) optimization using whole-genome gene expression profiles; or (4) gene expression profiles of cancer pathway genes.

Results

The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone.

Conclusion

The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology.