Albumin to creatinine ratio as a predictor to the severity of coronary artery disease

Introduction: Microalbuminuria (MA) is a well-known risk factor for coronary artery disease (CAD). It is associated with higher cardiovascular mortality, especially in diabetics. However, there are few data linking angiographic severity of CAD to MA.Aim: The aim of the present study was to assess the albumin to creatinine ratio as a new predictor for CAD and to correlate with its severity apart from other traditional CAD risk factors.Methods: Our study included 100 patients with documented CAD by coronary angiography in Alexandria main university hospital. The severity of CAD was scored on the basis of the number and the extent of lesions within the coronary arteries by using Syntax score. Urine albumin excretion was measured for all patients in morning spot urine samples by immune precipitation technique. We correlate between MA and severity of CAD.Results: In a total of 100 patients (74 males and 26 females), (mean age 55.71± 8.99 y) MA was present in 34 patients only. Patients were divided into two groups; group I included those without MA and group II with MA. CAD occurred more frequently in males than in females and in smokers than in non-smokers. There were no significant differences in the prevalence of hypertension and hypercholesterolemia between the two groups. A direct relationship between MA and extension of atherosclerotic coronary lesions was noticed (P = 0.009).Conclusion: Patients with MA having more severe angiographic CAD were compared to those without MA. This relation is independent of other risk factors. MA could be utilized as an independent risk factor for CAD.Keywords: Coronary artery disease (CAD); Microalbuminuria (MA); Albumin–creatinine rati

A rare case of locally advanced fibrosarcoma of diaphysal humerus managed successfully with limb-sparing procedures after neoadjuvant chemotherapy

Fibrosarcomas (FS) of bone are a rare malignancy accounting for less than 5% of all primary malignant bone neoplasms. Diagnosis and treatment approaches of this entity are complex and require a skilled and experienced multidisciplinary team

A high-performance 8 nV/root Hz 8-channel wearable and wireless system for real-time monitoring of bioelectrical signals

Background: It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals. Methods: To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. Results: To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented. Conclusions: The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Imaging of adult leukodystrophies

Leukodystrophies are genetically determined white matter disorders. Even though leukodystrophies essentially affect children in early infancy and childhood, these disorders may affect adults. In adults, leukodystrophies may present a distinct clinical and imaging presentation other than those found in childhood. Clinical awareness of late-onset leukodystrophies should be increased as new therapies emerge. MRI is a useful tool to evaluate white matter disorders and some characteristics findings can help the diagnosis of leukodystrophies. This review article briefly describes the imaging characteristics of the most common adult leukodystrophies

K-variant BCHE and pesticide exposure: Gene-environment interactions in a case-control study of Parkinson's disease in Egypt

Pesticide exposure is associated with increased risk of Parkinson's disease (PD). We investigated in Egypt whether common variants in genes involved in pesticide detoxification or transport might modify the risk of PD evoked by pesticide exposure. We recruited 416 PD patients and 445 controls. Information on environmental factors was collected by questionnaire-based structured interviews. Candidate single-nucleotide polymorphisms (SNPs) in 15 pesticide-related genes were genotyped. We analyzed the influence of environmental factors and SNPs as well as the interaction of pesticide exposure and SNPs on the risk of PD. The risk of PD was reduced by coffee consumption [OR = 0.63, 95% CI: 0.43-0.90, P = 0.013] and increased by pesticide exposure [OR = 7.09, 95% CI: 1.12-44.01, P = 0.036]. The SNP rs1126680 in the butyrylcholinesterase gene BCHE reduced the risk of PD irrespective of pesticide exposure [OR = 0.38, 95% CI: 0.20-0.70, P = 0.002]. The SNP rs1803274, defining K-variant BCHE, interacted significantly with pesticide exposure (P = 0.007) and increased the risk of PD only in pesticide-exposed individuals [OR = 2.49, 95% CI: 1.50-4.19, P = 0.0005]. The K-variant BCHE reduces serum activity of butyrylcholinesterase, a known bioscavenger for pesticides. Individuals with K-variant BCHE appear to have an increased risk for PD when exposed to pesticides