Assessment of thrombocytopenia, sleep apnea, and cardiac involvement in GNE myopathy patients.

INTRODUCTION: We previously identified UDP-N-acetylglucosamine 2-epimerase (GNE) myopathy patients with sleep apnea and a past history of thrombocytopenia, but without disease-specific cardiac involvement. This study aimed to clarify the occurrence, severity, and serial changes of these complications. METHODS: Thirty-three genetically confirmed GNE myopathy patients who participated in a 5-y longitudinal observational history study underwent platelet count and platelet-associated immunoglobulin G (PA-IgG) measurements, a sleep study, and electrocardiography (ECG), Holter ECG, and echocardiogram examinations. RESULTS: Among the 33 patients, three had low platelet counts and 17 out of 26 were PA-IgG positive. No patient exhibited bleeding tendencies, and 3 out of 28 had low platelet counts. Muscle weakness was more pronounced, and summed MMT and grip power significantly lower, in PA-IgG-positive patients than in PA-IgG-negative patients. Of 19 patients, 7, 4, and 3 who underwent a sleep study had mild, moderate, and severe sleep apnea, respectively, and three started continuous positive airway pressure (CPAP). The respiratory disturbance index was not significantly correlated with physical evaluation items or forced vital capacity. All patients underwent ECG, 32 underwent cardiac ultrasound, and 25 underwent Holter ECG. No disease-specific cardiac involvement was noted, no serial changes during the follow-up period were observed for ECG and echocardiography, and none of the patients required therapy for cardiac abnormalities. DISCUSSION: PA-IgG is a potential disease biomarker in GNE myopathy patients, although its significance needs to be clarified. While none of the patients in this study experienced cardiomyopathy or arrythmia due to myopathy, sleep apnea was identified as a frequent complication.

Four weeks of high-intensity training in moderate, but not mild hypoxia improves performance and running economy more than normoxic training in horses.

We investigated whether horses trained in moderate and mild hypoxia demonstrate greater improvement in performance and aerobic capacity compared to horses trained in normoxia and whether the acquired training effects are maintained after 2 weeks of post-hypoxic training in normoxia. Seven untrained Thoroughbred horses completed 4 weeks (3 sessions/week) of three training protocols, consisting of 2-min cantering at 95% maximal oxygen consumption V˙O2max under two hypoxic conditions (H16, FI O2  = 16%; H18, FI O2  = 18%) and in normoxia (N21, FI O2  = 21%), followed by 2 weeks of post-hypoxic training in normoxia, using a randomized crossover study design with a 3-month washout period. Incremental treadmill tests (IET) were conducted at week 0, 4, and 6. The effects of time and groups were analyzed using mixed models. Run time at IET increased in H16 and H18 compared to N21, while speed at V˙O2max was increased significantly only in H16. V˙O2max in all groups and cardiac output at exhaustion in H16 and H18 increased after 4 weeks of training, but were not significantly different between the three groups. In all groups, run time, V˙O2max , VV˙O2max , Q˙max , and lactate threshold did not decrease after 2 weeks of post-hypoxic training in normoxia. These results suggest that 4 weeks of training in moderate (H16), but not mild (H18) hypoxia elicits greater improvements in performance and running economy than normoxic training and that these effects are maintained for 2 weeks of post-hypoxic training in normoxia.

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study.

BACKGROUND: Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable. OBJECTIVE: We aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization. METHODS: Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort. RESULTS: Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively. CONCLUSIONS: For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

Assessment of early treatment response on MRI in multiple myeloma: Comparative study of whole-body diffusion-weighted and lumbar spinal MRI.

OBJECTIVES: To compare remission status at completion of chemotherapy for multiple myeloma (MM) with changes in total diffusion volume (tDV) calculated from whole-body diffusion-weighted imaging (WB-DWI) and fat fraction (FF) of lumbar bone marrow (BM) by modified Dixon Quant (mDixon Quant) soon after induction of chemotherapy, and to assess the predictive value of MRI. METHODS: Fifty patients (mean age, 66.9 ± 10.5 years) with symptomatic myeloma were examined before and after two cycles of chemotherapy. From WB-DWI data, tDV was obtained with the threshold for positive BM involvement. Mean FF was calculated from lumbar BM using the mDixon Quant sequence. At the completion of chemotherapy, patients were categorized into a CR/very good PR (VGPR) group (n = 15; mean age, 67.6 ± 10.3 years) and a PR, SD or PD group (n = 35; mean age, 69.1 ± 8.6 years). ROC curves were plotted to assess performance in predicting achievement of CR/VGPR. RESULTS: At second examination, serum M protein, ß2-microglobulin, and tDV were significantly decreased and hemoglobin, mean ADC, and FF were significantly increased in the CR/VGPR group and serum M protein was significantly increased in the PR/SD/PD group. The general linear model demonstrated that percentage changes in FF and M protein contributed significantly to achieving CR/VGPR (P = 0.02, P = 0.04, respectively). AUCs of ROC curves were 0.964 for FF and 0.847 for M protein. CONCLUSIONS: Early change in FF of lumbar BM and serum M protein soon after induction of chemotherapy contributed significantly to prediction of CR/VGPR.

The Value of a Cystatin C-based Estimated Glomerular Filtration Rate for Cardiovascular Assessment in a General Japanese Population: Results From the Iwate Tohoku Medical Megabank Project.

BACKGROUND: Epidemiological studies have shown that high circulating cystatin C is associated with a risk of cardiovascular disease (CVD) independent of creatinine-based renal function measurements. The present study investigated the comparison between the cystatin C-based estimated glomerular filtration rate (GFRcys) and creatinine-based GFR (GFRcr) to determine whether these measurements are associated with CV biomarkers and elevated CVD risk in a general Japanese population. METHODS: The Iwate Tohoku Medical Megabank Organization pooled individual participant data from a general population-based cohort study in Iwate prefecture (n = 29,375). Chronic kidney disease (CKD) was estimated using the GFRcys, GFRcr and the urine albumin-to-creatinine ratio (UACR). RESULTS: The prevalence of CKD in the participants was found to be higher based on the GFRcr than the GFRcys. Multiple variable analyses after adjusting for baseline characteristics showed that high-sensitivity cardiac troponin T (hs-cTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP) were associated with the GFRcys. The area under the receiver operating characteristic (AUROC) curve for identifying individuals with a high Suita score was higher for the GFRcys (AUROC = 0.68) than it was for the GFRcr (AUROC = 0.64, P < 0.001). The GFRcys provided reclassification improvement for the CVD risk prediction model by the GFRcr (net reclassification improvement = 0.341; integrated discrimination improvement = 0.018, respectively, P < 0.001). CONCLUSIONS: The GFRcys is more closely associated with CV biomarkers, including hs-cTnT and NT-proBNP levels, and a high Suita score than the GFRcr, and it provides additional value in the assessment of CVD risk using GFRcr.

Culture independent approach reveals domination of human-oriented microbes in a pharmaceutical manufacturing facility.

Strict microbial control is required in pharmaceutical manufacturing facilities, for which environmental microbial monitoring is fundamental. Appropriate microbial control is based on understanding the abundance and community structure of the microbes in the target environment, but most microbes are not culturable by conventional methods. Here, we determined the bacterial abundance and assessed the environmental microbiome in a pharmaceutical manufacturing facility using rRNA gene-targeted quantitative PCR (qPCR) and high-throughput sequencing of rRNA gene fragments. A commercially available microbial particle counter was also used for real-time measurements. In the air of the first gowning room and the passageway of the facility, the microbial particle number determined by both the particle counter and qPCR was ca. 104/m3; the number of microbial particles was about 100 times the number of culturable bacteria. Thus, the measurement of microbes using the particle counter was accurate. In the second gowning room of the facility, managed by a HEPA filter, the number of particles in the air was dependent on human movement, and was below the detection limit around 10 min after movement. Bacteria of the phyla Proteobacteria, Firmicutes, and Actinobacteria were frequently detected in samples from the facility; these bacteria are constituents of the human microbiota. Among fungi, Aspergillus and Cladosporium were detected in the air, and Malassezia was dominant on the walls. Our results provide fundamental data for the evaluation and control of microbes in pharmaceutical and food industry facilities.