Severe leukocytopenia due to copper deficiency induced by zinc supplementation in a patient on peritoneal dialysis: a case report.

Zinc deficiency is one cause of anemia. However, it has been reported that some patients who were treated with zinc supplementation to resolve this anemia subsequently experienced copper deficiency, which lead to continued anemia, as well as leukocytopenia and other symptoms. However, only two patients with copper deficiency induced by zinc supplementation undergoing peritoneal dialysis have been reported. Here, we report the case of a 59 year-old man with copper deficiency after zinc supplementation undergoing peritoneal dialysis (PD). He took meals only once a day and drank about 750 mL/day of wine every day. He had been receiving zinc supplementation for 4 months. He was diagnosed with severe leukocytopenia and worsening anemia at a planned outpatient visit; in addition, his copper levels had markedly decreased. Thus, zinc supplementation was discontinued, and the patient was instructed to take cocoa for copper supplementation. Because of severe leukocytopenia, he was admitted to our hospital, and granulocyte colony-stimulating factor was administered. Red blood cell transfusions were performed for anemia. After discontinuing zinc supplementation, his white blood cell count and hemoglobin levels improved.To avoid Cu deficiency, patients' dietary history should be checked in detail and Cu should be monitored carefully when Zn is supplemented in patients undergoing PD.

A Formula for the Estimation of 24-Hour Urinary Creatinine Excretion: A Derivation and Validation Study.

OBJECTIVE: Twenty-four-hour urinary creatinine (Cr) excretion (24h-uCr) is the basis of Cr clearance and urinary protein-Cr ratio, and it is related to frailty, worsening kidney function, and mortality in patients with chronic kidney disease. Although subjects with lower estimated glomerular filtration rate (eGFR) tend to have lower 24h-uCr, previous formulae for the estimation of 24h-uCr did not include Cr as a predictor. METHODS: This retrospective study included patients admitted to the Department of Nephrology at our hospital (derivation cohort and validation cohort: patients admitted between April 2016 and March 2020). The prediction formula of 24h-uCr was calculated using a multivariate linear regression model with the bootstrap method. Age, height, weight, sex, Cr, and cystatin C were used as predictors. RESULTS: The derivation and validation cohorts included 187 and 63 patients, respectively. The characteristics of the derivation and validation cohorts were as follows: age 73 (61-79.5) years and 70 (58.5-79) years; males, 61.5% and 60.3%; eGFRCr 27.0 (13.7-48.6) mL/min/1.73 m2 and 26.3 (14.0-51.5) mL/min/1.73 m2; and 24-hour urinary protein excretion 0.79 (0.17-2.12) g/day and 1.08 (0.26-2.55) g/day, respectively. Seven prediction formulae were derived. In all models, the Pearson's correlation coefficient was relatively high and statistically significant. However, previous models tended to overestimate the 24h-uCr. Furthermore, the predicted 24h-uCr calculated by the models that do not include Cr as a predictor fluctuates depending on the eGFRCr. CONCLUSION: The best formula for predicting 24h-uCr (mg/day) in a wide range of eGFR populations is a Cr-containing formula: [-9.04 × age (years) + 8.03 × weight (kg) + 0.66 × height (cm) + 188.59 (if male) - 32.11 × Cr (mg/dL) + 779.14].

Effects of dried natto diet on the transcript levels of the peroxisome proliferator-activated receptor-γ, coactivator-1α and -1ß, and nuclear receptor corepressor 1 genes in laying hens.

The effects of natto, a fermented soybean food, on transcript levels of hen peroxisome proliferator-activated receptor-γ (PPARG), PPARG coactivator-1α and -1ß (PPARGC1A and PPARGC1B), and nuclear receptor corepressor 1 (NCOR1) were investigated using real-time polymerase chain reaction in white leghorn (Julia strain) hens. Twenty-one- and 34-week-old hens were fed a basic or 3% dried natto-supplemented diet for 8 weeks. In the 21- and 34-week-old hens fed the natto-supplemented diet, hepatic PPARGC1B and NCOR1 transcript levels and adipose and hepatic PPARG transcript levels were significantly lower, respectively, than those in the control group. Furthermore, 34- and 42-week-old hens were fed a basic diet supplemented with 3% of the protein/fiber-enriched fraction (PFB) or 0.6% of the fat-enriched fraction (FAT) of natto, respectively, for 8 weeks. Adipose PPARG transcript levels were higher in the FAT diet group and significantly lower in the PFB diet group than in the control group. However, both FAT and PFB diet groups showed significantly lower hepatic PPARG transcript levels than did the control group. These results suggest that dried natto influences the transcript levels of PPARG, PPARGC1B, and NCOR1, and the FAT and PFB of natto influence the adipose and hepatic PPARG transcript levels in hens.

Validity of the total body centre of gravity during gait using a markerless motion capture system.

PURPOSE: The purpose of this study was to examine the validity of total body centre of gravity (COG) measurement during gait with markerless motion capture system (MLS) on the basis of values acquired with a marker-based motion capture system (MBS). MATERIALS AND METHODS: Thirty young healthy subjects walked on a flat surface as coordinate data from their bodies were acquired using the Kinect v2 (as a MLS) and Vicon systems (as a MBS). COG was calculated using coordinate data of the total body. Comparisons of COG ensemble curves in the mediolateral and vertical directions were performed between MLS and MBS throughout the gait cycle. The relative consistency between these systems was assessed using Pearson correlation coefficients. RESULTS: The COG trajectory made by using MLS data followed the trend of the COG trajectory with MBS in the mediolateral direction. In the vertical direction, however, the COG trajectories did not match between two systems. High correlation coefficients (r > 0.79) were observed from 30% to 80% of the gait cycle. The greatest difference of COG between MLS and MBS in the mediolateral direction was 1.1 mm. Differences in the vertical direction appeared to be proportional to the distance between the participant and the Kinect v2 sensor. CONCLUSION: In the mediolateral direction, COG calculated with MLS data during gait was validated with COG calculated on the basis of a MBS. Further correction of systematic error is necessary to improve the validity of COG calculations in the vertical direction.