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Total body irradiation (TBI) is a commonly used conditioning regimen for hematopoietic stem cell transplant (HCT), but dose heterogeneity and long-term organ toxicity pose significant challenges. Total marrow irradiation (TMI), an evolving radiation conditioning regimen for HCT can overcome the limitations of TBI by delivering the prescribed dose targeted to the bone marrow (BM) while sparing organs at risk. Recently, our group demonstrated that TMI up to 20 Gy in relapsed/refractory AML patients was feasible and efficacious, significantly improving 2-year overall survival compared to the standard treatment. Whether such dose escalation is feasible in elderly patients, and how the organ toxicity profile changes when switching to TMI in patients of all ages are critical questions that need to be addressed. We used our recently developed 3D image-guided preclinical TMI model and evaluated the radiation damage and its repair in key dose-limiting organs in young (~8 weeks) and old (~90 weeks) mice undergoing congenic bone marrow transplant (BMT). Engraftment was similar in both TMI and TBI-treated young and old mice. Dose escalation using TMI (12 to 16 Gy in two fractions) was well tolerated in mice of both age groups (90% survival ~12 Weeks post-BMT). In contrast, TBI at the higher dose of 16 Gy was particularly lethal in younger mice (0% survival ~2 weeks post-BMT) while old mice showed much more tolerance (75% survival ~13 weeks post-BMT) suggesting higher radio-resistance in aged organs. Histopathology confirmed worse acute and chronic organ damage in mice treated with TBI than TMI. As the damage was alleviated, the repair processes were augmented in the TMI-treated mice over TBI as measured by average villus height and a reduced ratio of relative mRNA levels of amphiregulin/epidermal growth factor (areg/egf). These findings suggest that organ sparing using TMI does not limit donor engraftment but significantly reduces normal tissue damage and preserves repair capacity with the potential for dose escalation in elderly patients.

Software Defined network (SDN) represents a sophisticated networking approach that separates the control logic from the data plane. This separation results in a loosely coupled architecture between the control and data planes, enhancing flexibility in managing and transforming network configurations. Additionally, SDN provides a centralized management model through the SDN controller, simplifying network administration. Despite these advantages, SDN has its security challenges. Issues such as topology spoofing, bandwidth exhaustion, flow table updates, and Distributed Denial of Service (DDoS) attacks are prevalent. Among these, DDoS attacks pose a significant threat to the SDN infrastructure. Understanding SDN's comprehensive ecosystem and functionality is crucial for mitigating SDN vulnerabilities that may attract DDoS attacks. Further, the central data controller of SDN becomes the primary target of DDoS attacks. In this article, we present: (i) A comprehensive SDN environment ecosystem with analysis of each class, (ii) A DDoS attacks taxonomy for the SDN environment with characterization of each class, (iii) Critically analyzed existing statistical, machine and deep learning-based DDoS attacks detection approaches for the SDN environment, (iv) Systematically characterize and compare existing open-source Distributed Processing Frameworks (DPF) for traffic engineering in the SDN environment, (v) Security challenges associated with the SDN environment, (vi) Summarize publically available DDoS attack datasets, (vii) Highlight open issues and future research directions for protecting the SDN environment from DDoS attacks. • Propose an SDN ecosystem taxonomy by exploring the SDN environment. • Explored DDoS detection mechanisms and distributed frameworks. • Analyze the existing DDoS Attack detection approaches in SDN. • Discuss and compare open-source distributed processing frameworks. • Identify and discuss security challenges associated with the SDN environment. [ABSTRACT FROM AUTHOR]

• A Hadoop based distributed detection framework called E-Had to detect DDoS attacks is proposed. • E-Had distribute computational and memory overheads to multiple mappers and reducers. • E-Had is robust as it can continue to work in case some of the mappers do not respond in time. • E-Had is implemented using HA-DDoS testbed consisting of 30 real systems. • E-Had has been validated using different attack scenarios of CAIDA and DDoSTB datasets. During the past few years, the traffic volume of legitimate traffic and attack traffic has increased manifolds up to Terabytes per second (Tbps). Because of the processing of such a huge traffic volume, it has become implausible to detect high rate attacks in time using conventional DDoS defense architectures. At present, the majority of the DDoS defense systems are deployed predominantly at the victim-end domain But these victim-end defense systems themselves are vulnerable to HR-DDoS attacks as the mammoth volume of attack traffic is generated by such type of attacks. The insufficient computational resources further make the problem more crucial at the victim-end. This paper proposed a distributed and collaborative architecture called E-Had that is capable of efficiently processing a large amount of data by distributing it among a number of mappers and reducers in a Hadoop based cluster. The proposed E-Had system has been comprehensively validated using various publicly available benchmarked datasets and real datasets generated in HA-DDoS testbed in terms of various detection system evaluation metrics. The experimental results clearly show that the proposed detection system is capable of early detection of different scenarios of DDoS attacks along with differentiating them from flash crowds. [ABSTRACT FROM AUTHOR]

This work is focused on synthesis and characterization of targeted magnetic nanoparticles as magnetic resonance imaging (МRI) agents for in vivo visualization of gliomas. Ferric oxide (Fe 3 O 4 ) cores were synthesized by thermal decomposition and coated with bovine serum albumin (BSA) to form nanoparticles with D eff of 53 ± 9 nm. The BSA was further cross-linked to improve colloidal stability. Monoclonal antibodies against vascular endothelial growth factor (mAbVEGF) were covalently conjugated to BSA through a polyethyleneglycol linker. Here we demonstrate that 1) BSA coated nanoparticles are stable and non-toxic to different cells at concentration up to 2.5 mg/mL; 2) conjugation of monoclonal antibodies to nanoparticles promotes their binding to VEGF-positive glioma С6 cells in vitro ; 3) targeted nanoparticles are effective in MRI visualization of the intracranial glioma. Thus, mAbVEGF-targeted BSA-coated magnetic nanoparticles are promising MRI contrast agents for glioma visualization. From the Clinical Editor This work focuses on synthesis and characterization of targeted magnetic nanoparticles as magnetic resonance imaging (МRI) agents for in vivo visualization of gliomas. The authors utilize the fact that high-grade gliomas have extensive areas of necrosis and hypoxia, which results in increased secretion of angiogenesis vascular endothelial growth factor (VEGF). Monoclonal antibodies against vascular endothelial growth factor (mAbVEGF) were covalently conjugated to crosslinked BSA coated ferric oxide (Fe3O4) nanoparticles. The results show that these targeted nanoparticles are effective in MRI visualization of the intracranial glioma and may provide a new and promising contrast agent. [ABSTRACT FROM AUTHOR]