Background: Cystinosis is a rare autosomal recessive lysosomal storage disease with high morbidity and mortality. It is caused by mutations in the CTNS gene that encodes the cystine transporter, cystinosin, which leads to lysosomal cystine accumulation. It is the major cause of inherited Fanconi syndrome, and should be suspected in young children with failure to thrive and signs of renal proximal tubular damage. The diagnosis can be missed in infants, because not all signs of renal Fanconi syndrome are present during the first months of life. Elevated white blood cell cystine content is the cornerstone of the diagnosis. Since chitotriosidase (CHIT1 or chitinase-1) is mainly produced by activated macrophages both in normal and inflammatory conditions which suggest that cystinosis should be included within the differential diagnosis of disorders associated with increased plasma chitotriosidase activity. This study is aimed to estimate serum chitotriosidase level, as a screening marker and therapeutic monitor for cystinosis disease in Iraqi children with cystinosis.
Subjects and Methods: The present study is a case-control study that included samples of 30 children with nephropathic cystinosis, compared to 25 healthy control children from those attending at The Genetic Rare Diseases Center / AL-Emamain AL-Kadhimain Teaching Hospital, Baghdad-Iraq.
Results: Our results reported that cystinotic children had a marked elevation of serum chitotriosidase activity, compared to age-matched healthy children, besides a significant associated with leukocyte-cystine content for cystinotic patients.
CHT1 as a Novel Biomarker
Conclusion: Estimation of serum chitotriosidase activity might aid in monitoring the therapeutic benefits of cysteamine therapy, as well as the prognosis of the disease when WBC cystine assessment is not available.
Key Words: Cystinosis, Cysteamine, Chitotriosidase.
Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (EEG and EMG signals) is proposed in this paper and are integrated with machine learning techniques to recognize the upper-limb motions of subjects. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrate that usin
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