Microglial activation and chronic neurodegeneration

Microglia, the resident innate immune cells in the brain, have long been implicated in the pathology of neurode-generative diseases. Accumulating evidence points to activated microglia as a chronic source of multiple neurotoxic factors, including tumor necrosis factor-α, nitric oxide, interleukin-1β, and reactive oxygen species (ROS), driving progressive neuron damage. Microglia can become chronically activated by either a single stimulus (e.g., lipopolysaccharide or neuron damage) or multiple stimuli exposures to result in cumulative neuronal loss with time. Although the mechanisms driving these phenomena are just beginning to be understood, reactive microgliosis (the microglial response to neuron damage) and ROS have been implicated as key mechanisms of chronic and neurotoxic microglial activation, particularly in the case of Parkinson’s disease. We review the mechanisms of neurotoxicity associated with chronic microglial activation and discuss the role of neuronal death and microglial ROS driving the chronic and toxic microglial phenotype

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings