Clinical, biochemical and molecular characterization of Wilson's disease in Moroccan patients.

Background: Wilson Disease (WD) is an autosomal recessive inherited metabolic disease caused by mutations in the ATP7B gene. WD is characterized by heterogeneous clinical presentations expressed by hepatic and neuropsychiatric phenotypes. The disease is difficult to diagnose, and misdiagnosed cases are commonly seen. Methods: In this study, the presented symptoms of WD, the biochemical parameters as well as its natural history are described based on cases collected in Mohammed VI Hospital University of Marrakech (Morocco). We screened and sequenced 21 exons of ATP7B gene from 12 WD patients that confirmed through biochemical diagnosis. Results: Mutational assessment of the ATP7B gene showed six homozygous mutations in 12 individuals however, 2 patients had no evidence of any mutation in promoter and exonic regions. All mutations are pathogenic and most were missense mutations. c.2507G > A (p.G836E), c.3694A > C (p.T1232P) and c.3310 T > C (p.C1104R) that were identified in 4 patients. The other mutations were a non-sense mutation (c.865C > T (p.C1104R)) detected in 2 patients, a splice mutation (c.51 + 4A > T) detected in 2 patients and a frameshift mutation (c.1746 dup (p.E583Rfs*25) detected in 2 patients. Conclusion: Our study is the first molecular analysis in Moroccan patients with Wilson's disease, the ATP7B mutational spectrum in the Moroccan population is diverse and still unexplored.

Urinary Glycosaminoglycans: Characterization and Quantification.

The growing body of evidence supports the potential of using urinary glycosaminoglycans (uGAGs) levels as biomarkers to guide diagnosis and as predictive biomarkers of treatment efficacy. Recently, studies have shown that, in addition to MPS, the prognosis and treatment of cancers and viral infections, including COVID-19, are enabled by characterization and/or traits by GAGs. Reliable and accessible detection and assay protocols of urinary GAGs are therefore of great support for laboratory workers and clinicians. Here we describe a semiquantitative and quantitative urinary glycosaminoglycans determination using 1,9-dimethylmethylene blue (DMB) and the characterization of uGAGs using thin layer chromatography (TLC).

Data Freshness and End-to-End Delay in Cross-Layer Two-Tier Linear IoT Networks.

The operational and technological structures of radio access networks have undergone tremendous changes in recent years. A displacement of priority from capacity-coverage optimization (to ensure data freshness) has emerged. Multiple radio access technology (multi-RAT) is a solution that addresses the exponential growth of traffic demands, providing degrees of freedom in meeting various performance goals, including energy efficiencies in IoT networks. The purpose of the present study was to investigate the possibility of leveraging multi-RAT to reduce each user's transmission delay while preserving the requisite quality of service (QoS) and maintaining the freshness of the received information via the age of information (AoI) metric. First, we investigated the coordination between a multi-hop network and a cellular network. Each IoT device served as an information source that generated packets (transmitting them toward the base station) and a relay (for packets generated upstream). We created a queuing system that included the network and MAC layers. We propose a framework comprised of various models and tools for forecasting network performances in terms of the end-to-end delay of ongoing flows and AoI. Finally, to highlight the benefits of our framework, we performed comprehensive simulations. In discussing these numerical results, insights regarding various aspects and metrics (parameter tuning, expected QoS, and performance) are made apparent.

D2D Mobile Relaying Meets NOMA-Part II: A Reinforcement Learning Perspective.

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today's mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system's performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading.

D2D Mobile Relaying Meets NOMA -Part I:A Biform Game Analysis.

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today's mobile networks. Such a communication paradigm requires implementing a certain level of intelligence at device level, allowing to interact with the environment and select proper decisions. However, decentralizing decision making sometimes may induce some paradoxical outcomes resulting, therefore, in a performance drop, which sustains the design of self-organizing, yet efficient systems. Here, each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. Given the set of active devices and the channel model, we derive the outage probability for both cellular link and D2D link, and compute the system throughput. We capture the device behavior using a biform game perspective. In the first part of this article, we analyze the pure and mixed Nash equilibria of the induced game where each device seeks to maximize its own throughput. Our framework allows us to analyse and predict the system's performance. The second part of this article is devoted to implement two Reinforcement Learning (RL) algorithms enabling devices to self-organize themselves and learn their equilibrium pure/mixed strategies, in a fully distributed fashion. Simulation results show that offloading the network by means of D2D-relaying improves per device throughput. Moreover, detailed analysis on how the network parameters affect the global performance is provided.

Update of a colorimetric method for quantitative determination of galactose in blood samples: A simple and rapid method for the early detection of inherited metabolic diseases.

BACKGROUND: A colorimetric microassay for the quantitative determination of galactose in the blood was taken and updated. This method helps in diagnosis and follow-up of several inherited metabolic diseases connected to galactose metabolism deficiency such as galactosemia, glycogenosis, glycosylation, tyrosinemia and citrin deficiency. Galactose assay in the blood presents difficulties due to interference with glucose. In this study, we update a method to get around these difficulties. METHOD: This procedure was based on the incubation of whole blood with orcinol in a strongly acidic solution to form a galactose and glucose complexes able to absorb at two different wavelengths. RESULTS: The standard curve analysis for the individual solutions of these two sugars showed a wide range of linearity from 0 to 200 mg / l. Under optimal experimental conditions, the stirring time of the orcinol is 3 minutes, the heating time of the reaction is 20 minutes at 56 ° C, and the duration of the incubation in the dark is 40 minutes. The analysis is carried out on fresh blood. The maximum absorbance of galactose and glucose is respectively 569 nm and 421 nm. An adapted diagnosis algorithm was developed based on our results. CONCLUSION: this method could help in screening and identifying patients with hypergalactosemia that need further investigations. It could represent a promising method for neonatal screening in countries with limited resources.

Usefulness of urinary glycosaminoglycans assay for a mucopolysaccharidosis-specific screening.

BACKGROUND: Mucopolysaccharidoses (MPS), a group of inherited metabolic disorders characterized by the accumulation of glycosaminoglycans, can be diagnosed early through newborn screening programs. Establishing newborn screening in Morocco is a challenging task for multiple economic and social reasons. Screening in a Moroccan population using 1,9-dimethylmethylene blue urinary glycosaminoglycan (GAG) assays may allow for an earlier diagnosis of MPS. We studied the feasibility of implementing screening in Moroccan children as an alternative to national newborn screening. We determined the reference ranges for GAGs in the Moroccan population, their stability during transport, the effectiveness of this test as a screening procedure for MPS in patients, and its use as a screening test for MPS in the Imssouane region, where the rate of consanguineous marriage is 38%. METHODS: Using dimethylmethylene blue assays, urine samples of 47 MPS patients were analyzed, together with urine samples from healthy controls (n = 368, age ranging from 1 month to 25 years), and from Imssouane region children (n = 350, age ranging from 6 months to 24 month). Precision, linearity, recovery, limits, and stability were tested. RESULTS: Urinary GAGs reference values are age and ethnicity dependent. The validation parameters established displayed great precision and accuracy leading to recoveries according to internationally accepted values for bioanalytical methods. Urinary GAGs were stable for a maximum of 7 weeks at 40 °C. Screening of Imssouane children resulted in the detection of a 6-month-old child, diagnosed with MPS I. CONCLUSIONS: Our results demonstrate the usefulness of quantifying glycosaminoglycans for early screening of MPS.

Implementation of an Affordable Method for MPS Diagnosis from Urine Screening to Enzymatic Confirmation: Results of a Pilot Study in Morocco.

BACKGROUND: Rapid and accurate diagnosis of mucopolysaccharidoses (MPS) is still a challenge due to poor access to screening and diagnostic methods and to their extensive clinical heterogeneity. The aim of this work is to perform laboratory biochemical testing for confirming the diagnosis of mucopolysaccharidosis (MPS) for the first time in Morocco. METHODS: Over a period of twelve months, 88 patients suspected of having Mucopolysaccharidosis (MPS) were referred to our laboratory. Quantitative and qualitative urine glycosaminoglycan (GAG) analyses were performed, and enzyme activity was assayed on dried blood spots (DBS) using fluorogenic substrates. Enzyme activity was measured as normal, low, or undetectable. RESULTS: Of the 88 patients studied, 26 were confirmed to have MPS; 19 MPS I (Hurler syndrome; OMIM #607014/Hurler-Scheie syndrome; OMIM #607015), 2 MPS II (Hunter syndrome; OMIM #309900), 2 MPS IIIA (Sanfilippo syndrome; OMIM #252900), 1 MPS IIIB (Sanfilippo syndrome; OMIM #252920) and 2 MPS VI (Maroteaux-Lamy syndrome; OMIM #253200). Parental consanguinity was present in 80.76% of cases. Qualitative urinary glycosaminoglycan (uGAGs) assays showed abnormal profiles in 31 cases, and further quantitative urinary GAG evaluation and Thin Layer Chromatography (TLC) provided important additional information about the likely MPS diagnosis. The final diagnosis was confirmed by specific enzyme activity analysis in the DBS samples. CONCLUSIONS: The present study shows that the adoption of combined urinary substrate analysis and enzyme assays using dried blood spots can facilitate such diagnosis, offer an important tool for an appropriate supporting care, and a specific therapy, when available.

Data Gathering and Energy Transfer Dilemma in UAV-Assisted Flying Access Network for IoT.

Recently, Unmanned Aerial Vehicles (UAVs) have emerged as an alternative solution to assist wireless networks, thanks to numerous advantages they offer in comparison to terrestrial fixed base stations. For instance, a UAV can be used to embed a flying base station providing an on-demand nomadic access to network services. A UAV can also be used to wirelessly recharge out-of-battery ground devices. In this paper, we aim to deal with both data collection and recharging depleted ground Internet-of-Things (IoT) devices through a UAV station used as a flying base station. To extend the network lifetime, we present a novel use of UAV with energy harvesting module and wireless recharging capabilities. However, the UAV is used as an energy source to empower depleted IoT devices. On one hand, the UAV charges depleted ground IoT devices under three policies: (1) low-battery first scheme; (2) high-battery first scheme; and (3) random scheme. On the other hand, the UAV station collects data from IoT devices that have sufficient energy to transmit their packets, and in the same phase, the UAV exploits the Radio Frequency (RF) signals transmitted by IoT devices to extract and harvest energy. Furthermore, and as the UAV station has a limited coverage time due to its energy constraints, we propose and investigate an efficient trade-off between ground users recharging time and data gathering time. Furthermore, we suggest to control and optimize the UAV trajectory in order to complete its travel within a minimum time, while minimizing the energy spent and/or enhancing the network lifetime. Extensive numerical results and simulations show how the system behaves under different scenarios and using various metrics in which we examine the added value of UAV with energy harvesting module.