How early can myocardial iron overload occur in Beta thalassemia major?

BACKGROUND: Myocardial siderosis is the most common cause of death in patients with beta thalassemia major(TM). This study aimed at investigating the occurrence, prevalence and severity of cardiac iron overload in a young Chinese population with beta TM. METHODS AND RESULTS: We analyzed T2* cardiac magnetic resonance (CMR), left ventricular ejection fraction (LVEF) and serum ferritin (SF) in 201 beta TM patients. The median age was 9 years old. Patients received an average of 13 units of blood per year. The median SF level was 4536 ng/ml and 165 patients (82.1%) had SF>2500 ng/ml. Myocardial iron overload was detected in 68 patients (33.8%) and severe myocardial iron overload was detected in 26 patients (12.6%). Twenty-two patients ≤10 years old had myocardial iron overload, three of whom were only 6 years old. No myocardial iron overload was detected under the age of 6 years. Median LVEF was 64% (measured by CMR in 175 patients). Five of 6 patients with a LVEF<56% and 8 of 10 patients with cardiac disease had myocardial iron overload. CONCLUSIONS: The TM patients under follow-up at this regional centre in China patients are younger than other reported cohorts, more poorly-chelated, and have a high burden of iron overload. Myocardial siderosis occurred in patients younger than previously reported, and was strongly associated with impaired LVEF and cardiac disease. For such poorly-chelated TM patients, our data shows that the first assessment of cardiac T2* should be performed as early as 6 years old

Electrochemical synthesis of ammonia from N2 and H2O based on (Li,Na,K)2CO3-Ce0.8Gd 0.18Ca0.02O2-δ composite electrolyte and CoFe2O4 cathode

Electrochemical synthesis of ammonia from water vapour and nitrogen was investigated using an electrolytic cell based on CoFe2O 4-Ce0.8Gd0.18Ca0.02O 2-δ (CFO-CGDC), CGDC-ternary carbonate composite and Sm 0.5Sr0.5CoO3-δ-Ce0.8Gd 0.18Ca0.02O2-δ (SSCo-CGDC) as cathode, electrolyte and anode respectively. CoFe2O4, CGDC and SCCo were prepared via a combined EDTA-citrate complexing sol-gel and characterised by X-ray diffraction (XRD). The AC ionic conductivities of the CGDC-carbonate composite were investigated under three different atmospheres (air, dry O 2 and wet 5% H2-Ar). A tri-layer electrolytic cell was fabricated by a cost-effective one-step dry-pressing and co-firing process. Ammonia was successfully synthesised from water vapour and nitrogen under atmospheric pressure and the maximum rate of ammonia production was found to be 6.5 × 10-11 mol s-1 cm-2 at 400 C and 1.6 V which is two orders of magnitude higher than that of previous report when ammonia was synthesised from N2 and H2O at 650 C

Deferasirox (Exjade®) significantly improves cardiac T2* in heavily iron-overloaded patients with β-thalassemia major

Noninvasive measurement of tissue iron levels can be assessed using T2* magnetic resonance imaging (MRI) to identify and monitor patients with iron overload. This study monitored cardiac siderosis using T2* MRI in a cohort of 19 heavily iron-overloaded patients with β-thalassemia major receiving iron chelation therapy with deferasirox over an 18-month period. Overall, deferasirox therapy significantly improved mean ± standard deviation cardiac T2* from a baseline of 17.2 ± 10.8 to 21.5 ± 12.8 ms (+25.0%; P = 0.02). A concomitant reduction in median serum ferritin from a baseline of 5,497 to 4,235 ng/mL (−23.0%; P = 0.001), and mean liver iron concentration from 24.2 ± 9.0 to 17.6 ± 12.9 mg Fe/g dry weight (−27.1%; P = 0.01) was also seen. Improvements were seen in patients with various degrees of cardiac siderosis, including those patients with a baseline cardiac T2* of <10 ms, indicative of high cardiac iron burden. These findings therefore support previous observations that deferasirox is effective in the removal of myocardial iron with concomitant reduction in total body iron

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Biopsy-based calibration of T2* magnetic resonance for estimation of liver iron concentration and comparison with R2 Ferriscan.

BACKGROUND: There is a need to standardise non-invasive measurements of liver iron concentrations (LIC) so clear inferences can be drawn about body iron levels that are associated with hepatic and extra-hepatic complications of iron overload. Since the first demonstration of an inverse relationship between biopsy LIC and liver magnetic resonance (MR) using a proof-of-concept T2* sequence, MR technology has advanced dramatically with a shorter minimum echo-time, closer inter-echo spacing and constant repetition time. These important advances allow more accurate calculation of liver T2* especially in patients with high LIC. METHODS: Here, we used an optimised liver T2* sequence calibrated against 50 liver biopsy samples on 25 patients with transfusional haemosiderosis using ordinary least squares linear regression, and assessed the method reproducibility in 96 scans over an LIC range up to 42 mg/g dry weight (dw) using Bland-Altman plots. Using mixed model linear regression we compared the new T2*-LIC with R2-LIC (Ferriscan) on 92 scans in 54 patients with transfusional haemosiderosis and examined method agreement using Bland-Altman approach. RESULTS: Strong linear correlation between ln(T2*) and ln(LIC) led to the calibration equation LIC = 31.94(T2*)-1.014. This yielded LIC values approximately 2.2 times higher than the proof-of-concept T2* method. Comparing this new T2*-LIC with the R2-LIC (Ferriscan) technique in 92 scans, we observed a close relationship between the two methods for values up to 10 mg/g dw, however the method agreement was poor. CONCLUSIONS: New calibration of T2* against liver biopsy estimates LIC in a reproducible way, correcting the proof-of-concept calibration by 2.2 times. Due to poor agreement, both methods should be used separately to diagnose or rule out liver iron overload in patients with increased ferritin

The relationship between cardiac and liver iron evaluated by MR imaging in haematological malignancies and chronic liver disease

Although iron overload is clinically significant, only limited data have been published on iron overload in haematological diseases. We investigated cardiac and liver iron accumulation by magnetic resonance imaging (MRI) in a cohort of 87 subjects who did not receive chelation, including 59 haematological patients. M-HIC (MRI-based hepatic iron concentration, normal values <36 μmol/g) is a non-invasive, liver biopsy-calibrated method to analyse iron concentration. This method, calibrated to R2 (transverse relaxation rate), was used as a reference standard (M-HIC(R2)). Transfusions and ferritin were evaluated. Mean M-HIC(R2) and cardiac R* of all patients were 142 μmol/g (95% CI, 114–170) and 36.4 1/s (95% CI, 34.2–38.5), respectively. M-HIC(R2) was higher in haematological patients than in patients with chronic liver disease or normal controls (P<0.001). Clearly elevated cardiac R2* was found in two myelodysplastic syndrome (MDS) patients with severe liver iron overload. A poor correlation was found between liver and cardiac iron (n=82, r=0.322, P=0.003), in contrast to a stronger correlation in MDS (n=7, r=0.905, P=0.005). In addition to transfusions, MDS seemed to be an independent factor in iron accumulation. In conclusion, the risk for cardiac iron overload in haematological diseases other than MDS is very low, despite the frequently found liver iron overload