THE SIGNIFICANCE OF MINIMAL RESIDUAL DISEASE IN ACUTE LYMPHOBLASTIC LEUKAEMIA: A SINGLE CENTRE STUDY

In Acute lymphoblastic Leukemia (ALL) assessment of molecular response to treatment, assessing minimal residual disease (MRD) is a major independent predictor of treatment outcome. Consequently, MRD is implemented in all ALL-treatment protocols to fill up or to redefine stratification of multifactorial risk with optional intensity of customized treatment. Aim: to specify the significance of MRD in the assessment of remission in children with ALL with results discordant between morphology and flow cytometry at the end of induction phase of therapy. Materials and Methods: A descriptive cross-sectional study was conducted at Jin Oncology Center from March 2019 through November 2023. Data were taken out of the records of 58 patients who had ALL less than 16 years old. All patients were less than 16 years old and treated by ukall. They were diagnosed using peripheral blood morphology, bone marrow study and/or flow cytometry when lymphoblasts in peripheral blood or bone marrow aspirate are ≥20% and was confirmed by flow cytometry. On 29th day of induction therapy, bone marrow was examined for morphology and flow cytometry. The presence or absence of MRD was determined, and CD19, CD10 and tdt were tested. By morphologic assessment they were divided patients into: Category 1, C1 (20% blasts). Statistical analysis was made using SPSS version 25. P value of less than 0.05 was considered significant. Results: The study involved 58 patients who had ALL. with a median age of 6.5 years, male to females ratio of 1.76:1, mean platelet count of 96.6 x 10⁹/L ,mean hemoglobin of 8.6 g/dL, mean leucocyte count of 74.3 x 10⁹/L), 48 cases (82.7%) of B-cell lineage and 10 cases (17.3%) of T-cell lineage, 94.6% of the B-cell cases were of the common B-ALL and the rest Pro-BALL type, 54.6% of the T-cell ALL was cortical T-ALL  and 44.4% Early T-cell ALL.  They were tested for MRD by morphology and flow cytometry on day 29. By morphology, 46 patients had remission but by flow only 24 cases. Seventeen cases had residual blasts >5%. In 19 cases there was a discrepancy between the results of morphology and flow. Twenty-five cases (52.08% of B-cell cases) were positive for MRD by flow results. Eight of the ten cases of T-ALL (80%), were positive for MRD by flow cytometry. Among 48 cases of B-ALL, 36 were in C1 category (morphologically in remission), 11 cases were in C2 category and one case in the C3 category. Of cases in C1 category, 17 were MRD +ve and 19 were MRD –ve by flow cytometry. In the C2 category, only 2 out of the 11 cases (18.18%) had discordant results between morphology and flow results. The correlation between morphology and flow results was 100% in the C3 category. Conclusion: MRD should not be the surrogate of morphology but to be used in conjunction in order to give us a more accurate representation of status of remission

Paraoxonase 2 protein is spatially expressed in the human placenta and selectively reduced in labour

Humans parturition involves interaction of hormonal, neurological, mechanical stretch and inflammatory pathways and the placenta plays a crucial role. The paraoxonases (PONs 1–3) protect against oxidative damage and lipid peroxidation, modulation of endoplasmic reticulum stress and regulation of apoptosis. Nothing is known about the role of PON2 in the placenta and labour. Since PON2 plays a role in oxidative stress and inflammation, both features of labour, we hypothesised that placental PON2 expression would alter during labour. PON2 was examined in placentas obtained from women who delivered by cesarean section and were not in labour and compared to the equivalent zone of placentas obtained from women who delivered vaginally following an uncomplicated labour. Samples were obtained from 12 sites within each placenta: 4 equally spaced apart pieces were sampled from the inner, middle and outer placental regions. PON2 expression was investigated by Western blotting and real time PCR. Two PON2 forms, one at 62 kDa and one at 43 kDa were found in all samples. No difference in protein expression of either isoform was found between the three sites in either the labour or non-labour group. At the middle site there was a highly significant decrease in PON2 expression in the labour group when compared to the non-labour group for both the 62 kDa form (p = 0.02) and the 43 kDa form (p = 0.006). No spatial differences were found within placentas at the mRNA level in either labour or non-labour. There was, paradoxically, an increase in PON2 mRNA in the labour group at the middle site only. This is the first report to describe changes in PON2 in the placenta in labour. The physiological and pathological significance of these remains to be elucidated but since PON2 is anti-inflammatory further studies are warranted to understand its role

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome

Molecular model of cytotoxin-1 from Naja mossambica mossambica venom in complex with chymotrypsin

Snake venom is a myriad of biologically active proteins and peptides. Three finger toxins are highly conserved in their molecular structure, but interestingly possess diverse biological functions. During the course of evolution the introduction of subtle mutations in loop regions and slight variations in the three dimensional structure, has resulted in their functional versatility. Cytotoxin-1(UniProt ID: P01467), isolated from Naja mossambica mossambica, showed the potential to inhibit chymotrypsin and the chymotryptic activity of the 20S proteasome. In the present work we describe a molec-ular model of cytotoxin-1 in complex with chymotrypsin, pre- pared by the online server ClusPro. Analysis of the molecular model shows that Cytotoxin-1(P01467) binds to chymotrypsin through its loop I located near the N -terminus. The concave side of loop I of the toxin fits well in the substrate binding pocket of the protease. We propose Phe" as the dedicated P1 site of the ligand. Being a potent inhibitor of the 20S proteasome, cytotoxin-1 (P01467) can serve as a potential antitumor agent. Already snake venom cytotoxins have been investigated for their ability as an anticancer agent. The molecular model of cytotoxin-1in complex with chymotrypsin provides important information towards understanding the complex formation

AI-based network-aware service function chain migration in 5G and beyond networks

Abstract While the 5G network technology is maturing and the number of commercial deployments is growing, the focus of the networking community is shifting to services and service delivery. 5G networks are designed to be a common platform for very distinct services with different characteristics. Network Slicing has been developed to offer service isolation between the different network offerings. Cloud-native services that are composed of a set of inter-dependent micro-services are assigned into their respective slices that usually span multiple service areas, network domains, and multiple data centers. Due to mobility events caused by moving end-users, slices with their assigned resources and services need to be re-scoped and re-provisioned. This leads to slice mobility whereby a slice moves between service areas and whereby the inter-dependent service and resources must be migrated to reduce system overhead and to ensure low-communication latency by following end-user mobility patterns. Recent advances in computational hardware, Artificial Intelligence, and Machine Learning have attracted interest within the communication community to study and experiment self-managed network slices. However, migrating a service instance of a slice remains an open and challenging process, given the needed co-ordination between inter-cloud resources, the dynamics, and constraints of inter-data center networks. For this purpose, we introduce a Deep Reinforcement Learning based agent that is using two different algorithms to optimize bandwidth allocations as well as to adjust the network usage to minimize slice migration overhead. We show that this approach results in significantly improved Quality of Experience. To validate our approach, we evaluate the agent under different configurations and in real-world settings and present the results

Toward using reinforcement learning for trigger selection in network slice mobility

Abstract Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility’s impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers’ multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods’ efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches

Optimization model for cross-domain network slices in 5g networks

Abstract Network Slicing (NS) is a key enabler of the upcoming 5G and beyond system, leveraging on both Network Function Virtualization (NFV) and Software Defined Networking (SDN), NS will enable a flexible deployment of Network Functions (NFs) belonging to multiple Service Function Chains (SFC) over various administrative and technological domains. Our novel architecture addresses the complexities and heterogeneities of verticals targeted by 5G systems, whereby each slice consists of a set of SFCs, and each SFC handles specific traffic within the slice. In this paper, we propose and evaluate a MILP optimization model to solve the complexities that arise from this new environment. Our proposed model enables a cost-optimal deployment of network slices allowing a mobile network operator to efficiently allocate the underlying layer resources according to its users’ requirements. We also design a greedy-based heuristic to investigate the possible trade-offs between execution runtime and network slice deployment. For each network slice, the proposed solution guarantees the required delay and the bandwidth, while efficiently handling the use of both the VNF nodes and the physical nodes, reducing the service provider’s Operating Expenditure (OPEX)

Fast Service Migration in 5G Trends and Scenarios

Abstract The need for faster and higher-capacity networks that can sustain modern, high-demanding applications has driven the development of 5G technology. Moreover, support for low-latency communication (1ms -10ms) is one of the main requirements of 5G systems. Multi-access Edge Computing (MEC) has been seen as a key component to attain the 5G objectives, since it allows hosting and executing critical services at the vicinity of users, thus reducing the latency to its minimum. Motivated by the evolution of real-time applications, we propose and evaluate two different mechanisms to improve the end-user experience by leveraging container-based live migration technologies. The first solution is aware of the users’ mobility patterns, while the other is oblivious to the users’ paths. Our results show approximately 50 percent reduction in downtime, which demonstrates the efficiency of the proposed solutions compared to prior works using similar underlying technology, i.e., LXC or Docker

Towards studying service function chain migration patterns in 5G networks and beyond

Abstract Given the indispensable need for a reliable network architecture to cope with 5G networks, 3GPP introduced a covet technology dubbed 5G Service Based Architecture (5G-SBA). Meanwhile, Multi-access Edge Computing (MEC) combined with SBA conveys a better experience to end-users by bringing application hosting from centralized data centers down to the network edge, closer to consumers and the data generated by applications. Both the 3GPP and the ETSI proposals offered numerous benefits, particularly the ability to deliver highly customizable services. Nevertheless, compared to large datacenters that tolerate the hosting of standard virtualization technologies (Virtual Machines (VMs) and servers), MEC nodes are characterized by lower computational resources, thus the debut of lightweight micro-service based applications. Motivated by the deficiency of current micro-services-based applications to support users’ mobility and assuming that all these issues are under the umbrella of Service Function Chain (SFC) migrations, we aim to introduce, explain and evaluate diverse SFC migration patterns. The obtained results demonstrate that there is no clear vanquisher, but selecting the right SFC migration pattern depends on users’ motion, applications’ requirements, and MEC nodes’ resources