Acute kidney injury in dogs following ingestion of cream of tartar and tamarinds and the connection to tartaric acid as the proposed toxic principle in grapes and raisins.

OBJECTIVE: To (1) describe exposure history, clinical signs, treatment, and diagnostic findings in 4 dogs following ingestion of tamarinds, and in 2 dogs following ingestion of cream of tartar, and (2) discuss tartaric acid, the common denominator, as the proposed toxic principle in tamarinds and grapes. SERIES SUMMARY: Reports in which dogs developed acute kidney injury following ingestion of cream of tartar or tamarinds were identified from the ASPCA Animal Poison Control Center electronic database. In these cases, decontamination was not performed, and treatments were delayed. Despite IV fluids and symptomatic and supportive care, 2 of the dogs became anuric and 1 became oliguric. Four dogs were euthanized, and the outcome is unknown for 2 of the dogs. Necropsies were performed on 3 of the dogs. Clinical signs, laboratory findings, and histopathologic lesions were similar to those reported in grape and raisin toxicosis. NEW OR UNIQUE INFORMATION PROVIDED: Acute kidney injury may develop following ingestion of cream of tartar or tamarinds in dogs. Connecting these reports with findings in grape and raisin toxicosis and the sensitivity to tartaric acid in dogs, tartaric acid is identified as the likely toxic component in grapes and tamarinds.

Insights on Spectral Measures for HRV Based on a Novel Approach for Data Acquisition.

In this paper, we present new insights on classical spectral measures for heart rate variability (HRV), based on a novel method for HRV acquisition. A dynamic breathing task, where the test participants are asked to breathe following a metronome with slowly increasing frequency, allows for the acquisition of respiratory-related HRV-data covering the frequency range in which adults breathe in different everyday situations. We discuss how the use of a time-frequency representation, e.g. the spectrogram or the Wigner-Ville distribution, should be preferred to the traditional use of the periodogram, due to the non-stationarity of the data. We argue that this approach can highlight the correlation of spectral measures such as low-frequency and high-frequency HRV with relevant factors as age, gender and Body-Mass-Index, thanks to the improved quality of the spectral measures.