Target Cell Selection Research Articles

Define Critical Parameters of Trastuzumab-Mediated ADCC Assays via Assay Optimization Processes, Focusing on the Impact of Cryopreserved Effector Cells on Assay Performance.

The mechanisms of mAb-induced ADCC have been well established. However, the ADCC bioassays used to quantify mAb-induced ADCC require continued development/refinement to properly assess and compare the potency of newly developed therapeutic mAbs and biosimilars to meet regulatory requirements. We used trastuzumab and a lactate dehydrogenase (LDH)-based ADCC bioassay as a model to define critical parameters of the ADCC bioassay, describing how several bioassay parameters, including preparation of effector cells, E/T ratio, target cell selection, bioassay media components, and treatment time can influence the data quality of the ADCC activity. We confirm that a 4 to 24 h recovery cultivation is required to restore peripheral blood mononuclear cells (PBMCs) and natural killer (NK) cell activity toward ADCC when using cryopreserved PBMCs. Furthermore, we delineated the cellular mechanisms underlying the restored ADCC activity following the recovery cultivation. We observed that CD69, an early marker of NK cell activation, was upregulated and a new subset CD56dim/CD16dim population was dramatically increased in the recovered NK cells, which led to an increase in expression and secretion of perforin, granzyme B, and cytokine production. This study provides comprehensive technical insights into ADCC bioassay optimization to inform trastuzumab biosimilar development. The knowledge gained from this study can also be leveraged to guide bioassay development for therapeutic mAbs with ADCC as the primary mechanism of action.

Automated myeloma cell selection using machine learning and artificial intelligence.

7565 Background: Precision of sampling is critical to achieve accurate results relevant to diagnostics and prognostics. Sampling errors may compromise the sensitivity and specificity of otherwise credible diagnostics technologies. Our 3- dimensional (3D) telomeres profiling methodology is conducted on individual target cells selected from the patient sample processed using 3D immuno-FISH. In our traditional workflow target cells are manually selected by highly trained personnel based on morphological characteristics and/ or immunophenotypic characteristics, namely cells positive for CD138 and CD56. However, the variability among trained operators required rounds of verification by a second and third operator and an approval by a Hematopathologist or the Lab Director to confirm the selected cells in some cases. Therefore, automated detection of myeloma cells is of paramount importance. Methods: In this study we employed machine learning and artificial intelligence (AI) tools to develop an automated algorithm capable of streamlining target cell (i.e. myeloma) selection and minimize or eliminate operator introduced variability. Training libraries were built using positive and negative cells. Over 5000 cells were used in the algorithm training process. We employed iterative rounds of validation and refinement to achieve a high precision automated tool. AI then identified myeloma cells in an automated fashion and the results of AI identification were confirmed by a Hematopathologist. Results: We report the results of a comparative analysis done on 20 myeloma patients at different stages of the disease including MGUS, smouldering myeloma and active myeloma. Also, the sample type included bone marrow smears, purified mono-nucleated plasma cells and bone marrow biopsies. 50 verified manually selected target cells and 50 target cells selected by the automated cell selection tool, approved by a skilled operator, from the same sample of each patient were analyzed using the TeloView platform. The TeloView platform quantifies 6 telomeric molecular and structural parameters. We conducted statistical analysis to calculate Coefficient of Variation (CV) between the results of each patient. We set the acceptable variability to < 15%. We achieved CVs of less than 10% among all patients included in the analysis. Conclusions: The automated cell selection tool streamlined this critical sampling process in our workflow minimizing operator-introduced variability, reducing the processing time to a fraction of the time required for manual cell selection and verification which enables high throughput for the 3D telomeres profiling while maintaining precision.

A study on 5G performance and fast conditional handover for public transit systems

Fifth-generation (5G) networks are now in a stable phase in terms of commercial release. 5G design is flexible to support a diverse range of radio bands (i.e., low-, mid-, and high-band) and application requirements. Since its initial roll-out in 2019, extensive measurements studies have revealed key aspects of commercial 5G deployments (e.g., coverage, signal strength, throughput, latency, handover, and power consumption among others) for several scenarios (e.g., pedestrian and car mobility, mid-, and high-bands, etc.). In this paper, we make a twofold contribution. First, we carry out an in-depth measurement study of 5G in a large public bus transit system in a major European city. Second, based on the insights observed with the measurement study, we propose a new target cell selection criteria applicable to Fast Conditional Handover (FCHO), a 3GPP-specific 5G technique to foster reliable mobility. Our results are based on an extensive measurement campaign performed with several mobile phones connected to several mobile network operators totaling more than 1500 km over three months. The measurements reveal how flexible the network deployment is by analyzing Radio Resource Control (RRC) messages, mobility management and the suitability of our FCHO solution, and application performance.

Adaptive cell selection algorithm for balancing cell loads in 5G heterogeneous networks

Heterogeneous networks (HetNets) are a promising solution for managing the exponential increase in the number of mobile users while maintaining high data rates and coverage. HetNets consist of various cell types with different cell coverage and system capacities. However, the traffic load in HetNets is variable, uneven and random over time, leading to unequal cell loads. Some cells have excessive user presence where the competition for system resources is high, while other cells have low user presence where system resources are not fully utilised. This paper proposes a mobility load balancing algorithm that prioritises millimetre-wave (mmWave) cells in target cell selection and user association to provide load balancing in 5G HetNets and improve overall system performance. A two-step target cell selection method that considers the load level of cells and reference signal received power (RSRP) is proposed. This method prioritises mmWave cells that meet cell load level and RSRP conditions in target cell selection, taking full advantage of the unique properties of mmWave cells (enhanced network throughput, spectral efficiency and enormous bandwidth) to balance traffic loads. The HO triggering and decision-making process is independently performed for each user. In case the serving cell is overloaded, different HO procedures are applied for load balancing depending on the target cell type (macro base station (BS) or mmWave BS). The scope of the study is further expanded by applying different HO procedures according to the serving and target cell types to maintain mobility robustness in case the serving cell is not overloaded. This research proposes various system scenarios with fixed HO margin (HOM) values and fixed time-to-trigger (TTT) durations to examine the effects of HCP settings on the proposed algorithm’s performance in terms of average load level of the serving cell, throughput, spectral efficiency and call dropping ratio (CDR). The system that provides the highest overall performance is applied to the proposed algorithm. To further assess and validate the performance of the proposed algorithm, it is compared with other load balancing algorithms from the literature. The simulation results reveal that the proposed algorithm does provide noticeable enhancements in network performance in terms of load level, throughput, spectral efficiency and CDR for various mobile speed scenarios as compared to existing load balancing algorithms.

Fuzzy Logic Based Handover Authentication in 5g Telecommunication Heterogeneous Networks

Under various deployment circumstances, fifth-generation (5G) telecommunications delivers improved network compound management with fast communication channels. Due to the introduction of the Internet of Things (IoT) in data management, the majority of the ultra-dense network models in 5G networks frequently have decreased spectral efficiency, weak handover management, and vulnerabilities. The majority of traditional handover authentication models are seriously threatened, making them vulnerable to a variety of security attacks. The authentication of networked devices is the most important issue. Therefore, a model that incorporates the handover mechanism and authentication model must be created. This article uses a fuzzy logic model to create a handover and key management system that focuses on cloud handover management and authentication performance. In order to decrease delays in 5G networks, the fuzzy logic is built with multiple criteria that aim to reduce the number of executed handovers and target cell selection. The simulation is run to evaluate the model’s performance in terms of latency, spatial complexity, and other metrics related to authentication attack validation.

Special issue on International Conference on Computing and Communication Networks

Special issue on International Conference on Computing and Communication Networks

Efficient handover protocol for 5G and beyond networks

The fifth generation (5G) and beyond 5G (B5G) networks offer ultra-low latencies, higher reliability, scalability, data rates and capacities to support applications such as vehicular communications, internet of everything (IoE) and device to device (D2D) communication. In spite of these excellent features, user privacy, resource management and handover authentications present some challenges. To facilitate seamless connectivity in 5G and B5G networks, numerous machine learning schemes have been developed to facilitate target cell selection based on parameters such as signal strength and signal to noise ratio (SNR). However, most of these approaches concentrate on performance enhancements, ignoring security and privacy issues. On their part, majority of the conventional handover authentication schemes exhibit long latencies which contravenes 5G and B5G requirements. Moreover, the base stations in these networks have very small footprints and hence require the deployment of numerous base stations within the coverage area. This serves to compound performance, security and privacy issues due to the resulting frequent handovers. In this paper, a multilayer neural network (MLNN) privacy and security preservation protocol is presented. To facilitate target cell selection, parameters that took user satisfaction, network, user equipment (UE) and service requirements into consideration were deployed so as to enhance both quality of service (QoS) and quality of experience (QoE) during and after handover. For handover security, timestamps, ephemerals and random nonces were deployed during handover authentication to offer both security and privacy. Formal security analysis using Burrows-Abadi-Needham (BAN) showed that the proposed protocol offered strong mutual authentication among the communicating entities. On the other hand, informal security analysis showed that the proposed protocol offers perfect forward key secrecy and is robust against attacks such as impersonation and packet replays. In addition, performance evaluation showed that it has the lowest communication costs and average computation overheads. Moreover, it exhibited a 27.1% increase in handover success rate, and a 24.1% reduction in ping pong rate.

A Generic Framework for Engineering Graph Canonization Algorithms

The state-of-the-art tools for practical graph canonization are all based on the individualization-refinement paradigm, and their difference is primarily in the choice of heuristics they include and in the actual tool implementation. It is thus not possible to make a direct comparison of how individual algorithmic ideas affect the performance on different graph classes. We present an algorithmic software framework that facilitates implementation of heuristics as independent extensions to a common core algorithm. It therefore becomes easy to perform a detailed comparison of the performance and behavior of different algorithmic ideas. Implementations are provided of a range of algorithms for tree traversal, target cell selection, and node invariant, including choices from the literature and new variations. The framework readily supports extraction and visualization of detailed data from separate algorithm executions for subsequent analysis and development of new heuristics. Using collections of different graph classes, we investigate the effect of varying the selections of heuristics, often revealing exactly which individual algorithmic choice is responsible for particularly good or bad performance. On several benchmark collections, including a newly proposed class of difficult instances, we additionally find that our implementation performs better than the current state-of-the-art tools.

Bandwidth Distribution Algorithm of Applications in LTE-Femtocell Network

The wireless communication has become the essential of today’s Internet of Things (IoT) and mobile applications which is increasing exponentially in near future. These applications demand high data rate to speed up the communication among IoT or internet applications. The data rate requirement of individual application is different. To fulfill these demands of every application Long Term Evolution (LTE) integrated with femtocell technology is identified as suitable wireless technology. The running applications on a user equipment changes dynamically. Hence in dense deployment of femtocell and IoT devices, the bandwidth distribution to many active applications becomes more complicated. For multiple running applications per device with available bandwidth constraint, optimal bandwidth allocation to applications has become the most challenging issue. We include the bandwidth allocation to applications during handover of user equipment in LTE-femtocell network. The handover procedure needs efficient cell selection criteria for target cell selection. Hence in our research we focus on bandwidth distribution among applications by proposing “Efficient Bandwidth Distribution” (EBD) mechanism and efficient cell selection by “Dynamic Cell Selection” (DCS) mechanisms to mitigate above mentioned issues.

Fuzzy-Neural based Cost Effective Handover Prediction Technique for 5G-IoT networks

Most of the existing works related to handover prediction in 5G networks, depends on huge mobility patterns collected over several periods of time, which will be tedious and complex to classify and analyze these patterns to predict the future locations of mobile users. Hence the main objective is to design a HO prediction technique which accurately predicts the next cell location with least amount of mobility history or patterns. In this paper, we design handoff prediction and target network selection scheme for 5G-IoT networks. For VHO triggering condition, Multi-layer Feed Forward Network (MFNN) is applied which will predict the user mobility based on distance, RSS, mobile speed and direction parameters. For target cell selection, Fuzzy decision model is applied based on the network level metrics such as traffic load, handover latency, battery power and user level metrics such as security and cost. The proposed approach will be implemented in NS3 and the performance is measured in terms of network throughput, handoff delay, handoff cost and prediction accuracy.

Plasmodium vivax ligand-receptor interaction: PvAMA-1 domain I contains the minimal regions for specific interaction with CD71+ reticulocytes

The malarial parasite’s invasion is complex, active and coordinated, involving many low and high affinity interactions with receptors on target cell membrane. Proteomics analysis has described around 40 proteins in P. vivax which could be involved in reticulocyte invasion; few have been studied with the aim of elucidating how many of them establish specific interactions with their respective host cells. Given the importance of knowing which of the parasite’s protein regions are functionally important for invasion, minimum regions mediating specific interaction between Plasmodium vivax apical membrane antigen 1 (PvAMA-1) and its host cell were here elucidated. The region covering PvAMA-1 domains I and II (PvAMA-DI-II) specifically bound to the CD71+ red blood cell subpopulation. A 20 residue-long region (81EVENAKYRIPAGRCPVFGKG100) located in domain I was capable of inhibiting PvAMA-DI-II recombinant protein binding to young reticulocytes (CD71+CD45−) and rosette formation. This conserved peptide specifically interacted with high affinity with reticulocytes (CD71+) through a neuraminidase- and chymotrypsin-treatment sensitive receptor. Such results showed that, despite AMA-1 having universal functions during late Plasmodium invasion stages, PvAMA-1 had reticulocyte-preferring binding regions, suggesting that P. vivax target cell selection is not just restricted to initial interactions but maintained throughout the erythrocyte invasion cycle, having important implications for designing a specific anti-P. vivax vaccine.

Exosomal tetraspanins mediate cancer metastasis by altering host microenvironment.

The metastases of malignant tumors develop through a cascade of events. The establishment of a pre-metastatic micro-environment is initiated by communication between tumors and host. Exosomes come into focus as the most potent intercellular communicators playing a pivotal role in this process. Cancer cells release exosomes into the extracellular environment prior to metastasis. Tetraspanin is a type of 4 times transmembrane proteins. It may be involved in cell motility, adhesion, morphogenesis, as well as cell and vesicular membrane fusion. The exosomal tetraspanin network is a molecular scaffold connecting various proteins for signaling transduction. The complex of tetraspanin-integrin determines the recruiting cancer exosomes to pre-metastatic sites. Tetraspanin is a key element for the target cell selection of exosomes uptake that may lead to the reprogramming of target cells. Reprogrammed target cells assist pre-metastatic niche formation. Previous reviews have described the biogenesis, secretion and intercellular interaction of exosomes in various tumors. However, there is a lack of reviews on the topic of exosomal tetraspanin in the context of cancer. In this review, we will describe the main characteristics of exosomal tetraspanin in cancer cells. We will also discuss how the cancer exosomal tetraspanin alters extracellular environment and regulates cancer metastasis.

Advanced handover scheme considering downlink and uplink service traffic in asymmetric channel

We study an application of link asymmetry between downlink and uplink in orthogonal frequency division multiple access (OFDMA) systems. In our previous works, we investigated downlink and uplink asymmetry and showed the advantages of utilizing the link asymmetry in link management. In this paper, we propose an advanced handover scheme that exploits multiple-criteria decision-making (MCDM) considering the asymmetric channel quality. The advanced handover scheme consists of the uplink estimation method and two handover target cell selection schemes. The uplink estimation method does not require additional radio resources to obtain uplink channel quality of neighbor cells, and the target cell selection schemes select an optimal target cell on the basis of the technique for ordering by similarity to the ideal solution (TOPSIS) algorithm with the downlink and uplink signal-to-interference-plus-noise ratio (SINR). The advanced handover scheme can be exploited to achieve two different purposes. First, the proposed scheme maximizes throughput on a network by determining weights on the basis of the downlink and uplink traffic load in a network. Second, the proposed scheme maximizes the quality of experience (QoE) from a user perspective by determining weights on the basis of the downlink and uplink traffic load ratio of a used service in a mobile device. We evaluate the performances of the proposed schemes from the perspectives of the network and a user by simulation. According to the results, we show that the proposed scheme outperforms previous studies in terms of network throughput, mean opinion score (MOS), and the number of calls dropped.

Yersinia pestis targets neutrophils via complement receptor 3.

Yersinia species display a tropism for lymphoid tissues during infection, and the bacteria select innate immune cells for delivery of cytotoxic effectors by the type III secretion system. Yet, the mechanism for target cell selection remains a mystery. Here we investigate the interaction of Yersinia pestis with murine splenocytes to identify factors that participate in the targeting process. We find that interactions with primary immune cells rely on multiple factors. First, the bacterial adhesin Ail is required for efficient targeting of neutrophils in vivo. However, Ail does not appear to directly mediate binding to a specific cell type. Instead, we find that host serum factors direct Y. pestis to specific innate immune cells, particularly neutrophils. Importantly, specificity towards neutrophils was increased in the absence of bacterial adhesins because of reduced targeting of other cell types, but this phenotype was only visible in the presence of mouse serum. Addition of antibodies against complement receptor 3 and CD14 blocked target cell selection, suggesting that a combination of host factors participate in steering bacteria towards neutrophils during plague infection.

A beacon-based handover scanning mechanism with QoS support in WiMAX femtocell architecture

WiMAX femtocell architecture has gained a lot of attention because of its better indoor service provision through traffic sharing between WiMAX and femtocell. However, since the signal of a WiMAX base station (BS) is usually stronger than that of a femtocell base station (fBS), the handover procedure of mobile stations may not be triggered due to the stronger signal association. And, a huge number of fBSs deployed within a WiMAX BS coverage area will require extra power to scan all fBSs for a handover determination. In this article, we propose a beacon-based handover scanning mechanism with Quality of Service (QoS) support. Through the modified beacons and MOB_NBR-ADV messages, a neighbor cell list with QoS parameters is generated automatically at each mobile station. The decision criteria for handover triggering and an appropriate target cell selection are also presented. The QualNet simulation results demonstrate that along with increased fBSs, the proposed mechanism can improve the total system throughput from 37% to 51% and obtain higher bandwidth utilization in the case of increasing number of fBSs, as compared with Full Scan and Neighbor Cell Information Mechanisms.

An algorithm for target cell selection based on cells' location relationship and coverage scope in LTE system

The mobility robustness optimisation in LTE system is a significant function entity to guarantee the performance of users' mobility by setting the parameters of handover, controlling terminals' handover and cell's selection. This paper first analyses all the problems brought about by the existing algorithm in the process of setting currently available parameters of mobility robustness, which only considers the level of terminals' moving rate. Then, it proposes an optimised algorithm for target cell selection combined with the location relationship of cells and coverage scope, and simulation results prove that the algorithm proposed in this paper can make cell selection more effective.

Toward tailored exosomes: The exosomal tetraspanin web contributes to target cell selection

Exosomes are discussed as potent therapeutics due to efficient transfer of proteins, mRNA and miRNA in selective targets. However, therapeutic exosome application requires knowledge on target structures to avoid undue delivery. Previous work suggesting exosomal tetraspanin–integrin complexes to be involved in target cell binding, we aimed to control this hypothesis and to define target cell ligands.Exosomes are rich in tetraspanins that associate besides other molecules with integrins. Co-immunoprecipitation of exosome lysates from rat tumor lines that differ only with respect to Tspan8 and beta4 revealed promiscuity of tetraspanin–integrin associations, but also few preferential interactions like that of Tspan8 with alpha4 and beta4 integrin chains. These minor differences in exosomal tetraspanin-complexes strongly influence target cell selection in vitro and in vivo, efficient exosome-uptake being seen in hematopoietic cells and solid organs. Exosomes expressing the Tspan8–alpha4 complex are most readily taken up by endothelial and pancreas cells, CD54 serving as a major ligand. Selectivity of uptake was confirmed with exosomes from an alpha4 cDNA transfected Tspan8+ lymph node stroma line. Distinct from exosomes from the parental line, the latter preferentially targeted endothelial cells and in vivo the pancreas. Importantly, pulldown experiments provided strong evidence that exosome-uptake occurs in internalization-prone membrane domains.This is the first report on the exosomal tetraspanin web contributing to target cell selection such that predictions can be made on potential targets, which will facilitate tailoring exosomes for drug delivery.

Exosome target cell selection and the importance of exosomal tetraspanins: a hypothesis

Exosomes are derived from limiting membranes of MVBs (multivesicular bodies). They carry and transfer selected membrane and cytoplasmic proteins, mRNA and microRNA into target cells. It is due to this shipping of information that exosomes are considered to be the most promising therapeutic tool for multiple diseases. However, whereas knowledge on the composition of exosomes is rapidly increasing, the mode of selective recruitment into exosomes as well as target cell selection is poorly understood. We suggest that at least part of this task is taken over by tetraspanins. Tetraspanins, which are involved in morphogenesis, fission and fusion processes, are enriched in exosomes, and our previous work revealed that the recruitment of distinct tetraspanins into exosomes follows very selective routes, including a rearrangement of the tetraspanin web. Furthermore, only exosomes expressing a defined set of tetraspanins and associated molecules target endothelial cells, thereby contributing to angiogenesis and vasculogenesis. On the basis of these findings we hypothesize (i) that the protein assembly of exosomes and possibly the recruitment of microRNA will be regulated to a large extent by tetraspanins and (ii) that tetraspanins account for target cell selection and the tight interaction/uptake of exosomes by the target cell. Exosomes herald an unanticipated powerful path of cell-cell communication. An answer to how exosomes collect and transfer information will allow the use of Nature's concept to cope with malfunctions.

Activation-induced internalization differs for the tetraspanins CD9 and Tspan8: Impact on tumor cell motility

Exosomes are most important intercellular communicators and tetraspanins/tetraspanin-complexes have been suggested to play an important role in exosomal target cell selection. We have shown that only exosomes expressing a Tspan8–CD49d complex preferentially bind endothelial cells, which initiates angiogenesis. This finding was unexpected as in the exosome donor cell Tspan8 is associated with CD49c and the tetraspanins CD9 and CD151. In view of the discussed therapeutic power of exosomes as message/drug transporter, it became important to clarify the mechanisms accounting for the distinct Tspan8-web in the cell membrane versus exosomes. We therefore compared the route of Tspan8 and Tspan8-chimera internalization, where the N- and/or C-terminal regions were exchanged with the corresponding regions of CD9 or CD151. Activation-induced Tspan8-internalization proceeds more rapidly than CD9 internalization and is accompanied by disassembly of the Tspan8–CD9–CD151 membrane complex in resting cells. Tspan8-internalization relies on the association of the Tspan8 N-terminal region with intersectin-2, a multimodular complex involved in clathrin-coated pit internalization. Internalization and recovery of Tspan8 in early endosomes is further promoted by the recruitment of CD49d such that only in PMA-activated cells a Tspan8–INS2–CD49d–clathrin complex is recovered in cholesterol-depletion-resistant membrane microdomains. PMA-induced Tspan8-internalization promotes cell migration, but reduces matrix and cell adhesion. Thus, stimulation initiates tetraspanin-web rearrangements, which have strong functional consequences for the cell, exosome-delivery and exosome target selection. This knowledge will be essential for generating tailored therapeutic exosomes.

Reovirus nonstructural protein σ1s is required for establishment of viremia and systemic dissemination

Serotype-specific patterns of reovirus disease in the CNS of newborn mice segregate with the viral S1 gene segment, which encodes attachment protein sigma1 and nonstructural protein sigma1s. The importance of receptor recognition in target cell selection by reovirus implicates the sigma1 protein as the primary effector of disease outcome. However, the contribution of sigma1s to reovirus disease is unknown. To define the function of sigma1s in reovirus pathogenesis, we generated a sigma1s-deficient virus by altering a single nucleotide to disrupt the sigma1s translational start site. Viruses were recovered that contain nine gene segments from strain type 3 Dearing and either the wild-type or sigma1s-null S1 gene segment from strain type 1 Lang. Following peroral inoculation of newborn mice, both viruses replicated in the intestine, although the wild-type virus achieved higher yields than the sigma1s-null virus. However, unlike the wild-type virus, the sigma1s-deficient virus failed to disseminate to sites of secondary viral replication, including the brain, heart, and liver. Within the small intestine, both viruses were detected in Peyer's patches, but only the wild-type virus reached the mesenteric lymph node. Concordantly, wild-type virus, but not sigma1s-deficient virus, was detected in the blood of infected animals. Wild-type and sigma1s-null viruses produced equivalent titers following intracranial inoculation, indicating that sigma1s is dispensable for viral growth in the murine CNS. These results suggest a key role for sigma1s in virus spread from intestinal lymphatics to the bloodstream, thereby allowing the establishment of viremia and dissemination to sites of secondary replication within the infected host.