Acetaminophen improves tardive akathisia induced by dopamine D₂ receptor antagonists

Tardive akathisia is a movement disorder characterized by internal restlessness with an uncontrollable urge to move, leading to repetitive movements. It is a common side effect of long-term treatment with dopamine D₂ receptor antagonists. In the present study, we analyzed the FDA Adverse Event Reporting System and IBM MarketScan Research Database to find a drug that can be used concomitantly with dopamine D₂ receptor antagonists and still reduce the risk of akathisia. Acetaminophen was determined to be the most effective akathisia-suppressing drug. In an experimental validation of the hypothesis, chronic treatment of rats with haloperidol caused akathisia symptoms, including increased stereotyped behavior and locomotor activity, and decreased immobility time. Acute treatment with acetaminophen significantly attenuated haloperidol-induced akathisia. In the ventral striata of these rats, acetaminophen prevented haloperidol-induced decrease in the number of c-Fos⁺ preproenkephalin⁺ neurons. These results suggest that acetaminophen is effective in suppressing tardive akathisia by activating indirect-pathway medium spiny neurons

Identifying dynamical systems with bifurcations from noisy partial observation

Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation structure.Comment: 16 pages, 6 figure

Gene transfer of GLT-1, a glial glutamate transporter, into the spinal cord by recombinant adenovirus attenuates inflammatory and neuropathic pain in rats

<p>Abstract</p> <p>Background</p> <p>The glial glutamate transporter GLT-1 is abundantly expressed in astrocytes and is crucial for glutamate removal from the synaptic cleft. Decreases in glutamate uptake activity and expression of spinal glutamate transporters are reported in animal models of pathological pain. However, the lack of available specific inhibitors and/or activators for GLT-1 makes it difficult to determine the roles of spinal GLT-1 in inflammatory and neuropathic pain. In this study, we examined the effect of gene transfer of GLT-1 into the spinal cord with recombinant adenoviruses on the inflammatory and neuropathic pain in rats.</p> <p>Results</p> <p>Intraspinal infusion of adenoviral vectors expressing the GLT-1 gene increased GLT-1 expression in the spinal cord 2–21 days after the infusion. Transgene expression was primarily localized to astrocytes. The spinal GLT-1 gene transfer had no effect on acute mechanical and thermal nociceptive responses in naive rats, whereas it significantly reduced the inflammatory mechanical hyperalgesia induced by hindlimb intraplantar injection of carrageenan/kaolin. Spinal GLT-1 gene transfer 7 days before partial sciatic nerve ligation recovered the extent of the spinal GLT-1 expression in the membrane fraction that was decreased following the nerve ligation, and prevented the induction of tactile allodynia. However, the partial sciatic nerve ligation-induced allodynia was not reversed when the adenoviruses were infused 7 or 14 days after the nerve ligation.</p> <p>Conclusion</p> <p>These results suggest that overexpression of GLT-1 on astrocytes in the spinal cord by recombinant adenoviruses attenuates the induction, but not maintenance, of inflammatory and neuropathic pain, probably by preventing the induction of central sensitization, without affecting acute pain sensation. Upregulation or functional enhancement of spinal GLT-1 could be a novel strategy for the prevention of pathological pain.</p

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data

INTRODUCTION: Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. OBJECTIVE: This study aimed to identify methods for the early detection of a wide range of ADR signals. METHODS: First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. RESULTS: We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. CONCLUSIONS: ARM of claims data may be effective in the early detection of a wide range of ADR signals