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Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
The taxon Prosopis (Leguminosae, mesquite) includes some of the most common tree species in the dry tropics (Pasiecznik et al., 2004). Several species, all of American origin, have been intentionally distributed throughout the tropical world, because of their acclaimed roles as fast-growing, drought-tolerant, multipurpose trees. They are valued as a rehabilitation tool for degraded rangelands, shade, fodder (the pods are palatable for livestock and humans), honey, charcoal, timber, fuel, and several other resources (Fagg and Stewart, 1994; Felker and Moss, 1996; Pasiecznik et al., 2001). Large-scale systematic plantings have been made in parts of Africa, several oceanic islands, the Middle East and the Indian subcontinent since the mid 1900s, but ad hoc introductions have been common since the early nineteenth century (Harding, 1988; Fagg and Stewart, 1994; Felker and Moss, 1996; Tewari et al., 1998; Pasiecznik et al., 2001; van Klinken and Campbell, 2001; Mauremootoo, 2006; Ogutu and Mauremootoo, 2006).
Introduced mesquite species have become invasive in many countries, while some species are a nuisance to humans and livestock within their native ranges (DeLoach, 1985; Dussart et al., 1998). As a result, mesquite is now seen to be causing substantial negative economic, environmental, and social impacts over large parts of the world (van Klinken and Campbell, 2001; Mauremootoo, 2006; Ogutu and Mauremootoo, 2006; Zimmermann et al., 2006). However, indications are that the per capita impacts of invasive populations could be greater in their exotic ranges.
A new type of AFM is presented which allows for direct measurements of nanomechanical properties in ultra-high vacuum and liquid environments. The AFM is also capable of atomic-scale imaging of force gradients. This is achieved by vibrating a stiff lever at very small amplitudes of less than 1 Å (peak-to-peak) at a sub-resonance amplitude. This linearizes the measurement and makes the interpretation of the data straight-forward. At the atomic scale, interaction force gradients are measured which are consistent with the observation of single atomic bonds. Also, atomic scale damping is observed which rapidly rises with the tip-sample separation. A mechanism is proposed to explain this damping in terms of atomic relaxation in the tip. We also present recent results in water where we were able to measure the mechanical response due to the molecular ordering of water close to an atomically flat surface.
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