Background: Amyotrophic lateral sclerosis (ALS) is an incurable disease. There are no defined valid biomarkers for evaluating therapeutic outcomes of this disease. Numerous approaches have been made to meet this impact. Stem cells, one of these approaches, that might give various hopeful results that can be interpreted as a treatment for ALS This study aims to evaluate Mesenchymal Stem Cell (MSC) therapy as a promising novel treatment modality by estimating some parameters, such as immunological and biochemical factors. Methods: This study course involved 15 confirmed patients with ALS and was designed as an open-label, one-arm retrospective study to evaluate potential diagnostic biomarkers of repeated infusions of autologous bone marrow-derived mesenchymal stem cells (BM-MSC) administered at a dose of 1×106cells/kg BW with 1-month interval, in equal amounts in both intravenous (IV) and intrathecal (IT) simultaneously, by various biochemical and immunological parameters were evaluated during the three-month follow-up period. Results: Our study indicated that in the case of Immunological biomarkers ,Tumor Necrosis Factor-alpha (TNF-α) levels in the Cerebrospinal fluid (CSF) showed a significant decrease at month three after transplantation compared to levels at month zero, and the P-value was (P<0.01). No statistically significant changes were observed for other immunological and biochemical parameters and P-value (P>0.05). Conclusions: These results can indicate the benefit of stem cell transfusion in patients with ALS and suggest some diagnostic biomarkers. Several studies are required to approve these results.

Particulate matter (PM) contains heavy metals that affect various cellular functions and gene expression associated with an array of acute and chronic diseases, in humans. However, their specific effects on the stem cells remain unclear. Here, we report the effects of PM collected from Jeddah city on bone marrow mesenchymal stem cells (BM-MSCs) on proliferation, cell death, related gene expression and systems biological analysis aiming to understand the underlying mechanisms. Two different sizes (PM_2.5-₁₀) were tested in vitro at various concentrations (15 to 300 µg/ml) and durations (24 to 72 h). PMs induced cellular stress including membrane damage, shrinkage and death. Lower concentrations of PM_2.5 increased BM-MSCs proliferation, while higher concentrations decreased it. PM₁₀ decreased BM-MSCs proliferation in a concentration-dependant manner. The X-Ray Fluorescence spectrometric analysis showed that PM contains high levels of heavy metals. Ingenuity Pathway Analysis (IPA) and hierarchical clustering analyses showed that heavy metals were associated with signalling pathways involving cell stress/death, cancer and chronic diseases. qRT-PCR results showed differential regulation of the apoptosis genes (BCL2, BAX); upregulation of inflammation associated genes (TNF-a and IL-6) and downregulation of cell cycle regulation gene (P53). We conclude that PM could affect different cellular functions and predispose to debilitating diseases.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

Many statistical distributions approximate well the frequent values of the maximum discharges, but have a very large spread for medium or rare probabilities of exceedance. This scattering defines a range of uncertainty of maximum discharges outside the measured values. Based on the upper (U) and lower (L) values of the uncertainty interval, a maximum discharge flood (MDF) and a maximum volume flood (MVF) are defined for each probability of exceedance. This approach is in agreement with the bivariate analysis, the contour lines for a certain probability of exceedance putting into evidence an infinity of couples (maximum discharge, flood volume). If the most critical combinations are selected, the MDF and MVF are derived. Apart from maximum discharge and flood volume, the shape of the design flood, characterized by the time to peak and the total duration, is also important. The easiest way to obtain the design flood is to use an analytical curve that passes through the characteristic points of the flood. Another possibility, which was largely developed in this paper, is to normalize the floods, then to define clusters of floods with similar shapes, and to obtain an average dimensionless flood for each class. Finally, a family of design floods with different shapes, but characterized by the same parameters for each probability of exceedance, are derived. The use of these floods in the design or operation of hydrotechnical works or in the delineation of flooded areas is presented at the end.