A prognostic model to identify short survival expectancy of medical oncology patients at the time of hospital discharge.

BACKGROUND: Hospitalization of cancer patients is associated with poor overall survival, but prognostic misclassification may lead to suboptimal therapeutic decisions and transitions of care. No model is currently available for stratifying the heterogeneous population of oncological patients after a hospital admission to a general Medical Oncology ward. We developed a multivariable prognostic model based on readily available and objective clinical data to estimate survival in oncological patients after hospital discharge. METHODS: A multivariable model and nomogram for overall survival after hospital discharge was developed in a retrospective training cohort and prospectively validated in an independent set of adult patients with solid tumors and a first admission to a unit of medical oncology. Performance of the model was assessed by C-index and Kaplan-Meier survival curves stratified by risk categories. RESULTS: From a population of 1089 patients with a first hospitalization, 757 patients were included in the training group [median survival, 43 weeks; 95% confidence interval (CI), 37-51 weeks] and 200 patients in the validation cohort (median survival, 44 weeks; 95% CI, 34 weeks-not reached). An accelerated failure time log-normal model was built, including five variables (primary tumor, stage, cause of admission, active treatment, and age). The C-index was 0.71 (95% CI, 0.69-0.73), with a good calibration, and adequate validation in the prospective cohort (C-index: 0.69; 95% CI, 0.65-0.74). Median survival in three predefined model-based risk groups was 10.7 weeks (high), 27.0 weeks (intermediate), and 3 years (low) in the training cohort, with comparable values in the validation cohort. CONCLUSIONS: In oncological patients, individualized predictions of survival after hospitalization were provided by a simple and validated model. Further evaluation of the model might determine whether its use improves shared decision making at discharge.

Mesophyll conductance to CO2 and Rubisco as targets for improving intrinsic water use efficiency in C3 plants.

Water limitation is a major global constraint for plant productivity that is likely to be exacerbated by climate change. Hence, improving plant water use efficiency (WUE) has become a major goal for the near future. At the leaf level, WUE is the ratio between photosynthesis and transpiration. Maintaining high photosynthesis under water stress, while improving WUE requires either increasing mesophyll conductance (gm ) and/or improving the biochemical capacity for CO2 assimilation-in which Rubisco properties play a key role, especially in C3 plants at current atmospheric CO2 . The goals of the present analysis are: (1) to summarize the evidence that improving gm and/or Rubisco can result in increased WUE; (2) to review the degree of success of early attempts to genetically manipulate gm or Rubisco; (3) to analyse how gm , gsw and the Rubisco's maximum velocity (Vcmax ) co-vary across different plant species in well-watered and drought-stressed conditions; (4) to examine how these variations cause differences in WUE and what is the overall extent of variation in individual determinants of WUE; and finally, (5) to use simulation analysis to provide a theoretical framework for the possible control of WUE by gm and Rubisco catalytic constants vis-à-vis gsw under water limitations.

Diffusional limitations explain the lower photosynthetic capacity of ferns as compared with angiosperms in a common garden study.

Ferns are thought to have lower photosynthetic rates than angiosperms and they lack fine stomatal regulation. However, no study has directly compared photosynthesis in plants of both groups grown under optimal conditions in a common environment. We present a common garden comparison of seven angiosperms and seven ferns paired by habitat preference, with the aims of (1) confirming that ferns do have lower photosynthesis capacity than angiosperms and quantifying these differences; (2) determining the importance of diffusional versus biochemical limitations; and (3) analysing the potential implication of leaf anatomical traits in setting the photosynthesis capacity in both groups. On average, the photosynthetic rate of ferns was about half that of angiosperms, and they exhibited lower stomatal and mesophyll conductance to CO2 (gm ), maximum velocity of carboxylation and electron transport rate. A quantitative limitation analysis revealed that stomatal and mesophyll conductances were co-responsible for the lower photosynthesis of ferns as compared with angiosperms. However, gm alone was the most constraining factor for photosynthesis in ferns. Consistently, leaf anatomy showed important differences between angiosperms and ferns, especially in cell wall thickness and the surface of chloroplasts exposed to intercellular air spaces.

Rubisco catalytic properties optimized for present and future climatic conditions.

Because of its catalytic inefficiencies, Rubisco is the most obvious target for improvement to enhance the photosynthetic capacity of plants. Two hypotheses are tested in the present work: (1) existing Rubiscos have optimal kinetic properties to maximize photosynthetic carbon assimilation in existing higher plants; (2) current knowledge allows proposal of changes to kinetic properties to make Rubiscos more suited to changed conditions in chloroplasts that are likely to occur with climate change. The catalytic mechanism of Rubisco results in higher catalytic rates of carboxylation being associated with decreased affinity for CO2, so that selection for different environments involves a trade-off between these two properties. The simulations performed in this study confirm that the optimality of Rubisco kinetics depends on the species and the environmental conditions. In particular, environmental drivers affecting the CO2 availability for carboxylation (Cc) or directly shifting the photosynthetic limitations between Rubisco and RuBP regeneration determine to what extend Rubisco kinetics are optimally suited to maximize CO2 assimilation rate. In general, modeled values for optimal kinetic reflect the predominant environmental conditions currently encountered by the species in the field. Under future climatic conditions, photosynthetic CO2 assimilation will be limited by RuBP-regeneration, especially in the absence of water stress, the largest rise in [CO2] and the lowest increases in temperature. Under these conditions, the model predicts that optimal Rubisco should have high Sc/o and low kcat(c).