Prediction of disability-free survival in healthy older people - Johannes Tobias Neumann; Le T. P. Thao; Anne M. Murray; Emily Callander; Prudence R. Carr; Mark R. Nelson; Rory Wolfe; Robyn L. Woods; Christopher M. Reid; Raj C. Shah; Anne B. Newman; Jeff D. Williamson; et al. (GeroScience, 2022)
Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people. Trial registration Clinicaltrials.gov (NCT01038583)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Evaluation of a new chromogenic medium, chromID OXA-48, for recovery of carbapenemase-producing Enterobacteriaceae from patients at a university hospital in Turkey - P. Zarakolu; K. M. Day; H. E. Sidjabat; W. Kamolvit; C. V. Lanyon; S. P. Cummings; D. L. Paterson; M. Akova; J. D. Perry (European Journal of Clinical Microbiology & Infectious Diseases, 2015)

Debate: “Is Increasing Neuroinflammation Beneficial for Neural Repair?” - Keith A. Crutcher; Howard E. Gendelman; Jonathan Kipnis; J. Regino Perez-Polo; V. H. Perry; Phillip G. Popovich; Lynne C. Weaver (Journal of Neuroimmune Pharmacology, 2006)

Chromosome size and origin as determinants of the level of CENP-A incorporation into human centromeres - Danielle V. Irvine; David J. Amor; Jo Perry; Nicolas Sirvent; Florence Pedeutour; K. H. Andy Choo; Richard Saffery (Chromosome Research, 2004)

Neuroscience data and tool sharing - Peter Eckersley; Gary F. Egan; Erik De Schutter; Tang Yiyuan; Mirko Novak; Vaclav Sebesta; Line Matthiessen; Irio P. Jaaskelainen; Ulla Ruotsalainen; Andreas V. M. Herz; Klaus-Peter Hoffmann; Raphael Ritz; et al. (Neuroinformatics, 2003)

The 10th Conference of the Entomological Society of Israel - Rachel Galun; I. Ishaaya; Sara Yablonski; Zmira Mendelson; Y. Mansour; Svetlana Kontsedalov; U. Pluschkell; A. R. Horowitz; A. R. Horowitz; Sara Yablonski; Zmira Mendelson; I. Ishaaya; et al. (Phytoparasitica, 1997)

s of papers presented at the Forty-First annual meeting - Jay L. Haddock; Ray D. Jackson; W. O. Harrington; R. L. Olson; W. J. Weston; Mary L. Belote; W. J. Hooker; A. P. Benson; R. W. Hougas; S. J. Poloquin; R. W. Ross; G. V. C. Houghland; et al. (American Journal of Potato Research, 1958)