Validation Platform Research Articles

Resolving neuroinflammation in AD

AbstractBackgroundHuman genetics point towards a key role of microglia and microglia expressed genes like TREM2 in neuroinflammation and AD pathology, yet our understanding of microglia spatial and temporal dynamics, therapeutic directionality and translational biomarkers remains limited.Method and ResultsMuna Therapeutics applies structural biology, computational chemistry and human IPSC derived cell assays to develop small molecule agonists for microglial drug targets including TREM2. Introduction of human mutations allows specific drug – target interactions to be probed and has revealed novel small molecule binding pockets, enabling optimized drug design. To identify novel drug targets involved in neuroinflammation, we have built an all‐in‐human spatial transcriptomics and bioinformatics‐based discovery and validation platform. We have assessed >50 human brain samples for pathology‐induced changes in gene expression in microglia and other cells from samples with AD pathology from patients with and without cognitive impairment. This approach has revealed patterns of differentially expressed genes and pathways in brains linked to pathological hallmarks such as Ab plaques and tau tangles, as well as to resilience to the effects of these pathologies. Candidate targets and mechanisms are tested for their impact on neuroinflammation, including directionality, in humanized model systems that also enable translational biomarker discovery and validation.ConclusionAt Muna Tx, state‐of‐the‐art target discovery and validation as well as structural biology and computational chemistry platforms enable microglia targeted drug discovery and can accelerate therapeutic advancement to clinical development.

Thimet Oligopeptidase is a potential CSF biomarker for Alzheimer’s Disease

AbstractBackgroundUsing antibody‐based Olink proteomics, we recently established that Thimet Oligopeptidase (THOP1) is a potential cerebrospinal fluid (CSF) biomarker for Alzheimer’s Disease (AD). THOP1 is a neuropeptidase able to degradate Aβ peptides. Here, we aimed to develop specific THOP1 immunoassays for further clinical validation and to enable future implementation of this biomarker.MethodWe developed and compared novel immunoassays for CSF THOP1 analysis on automated Ella™ and Simoa™ platforms. After analytical validation, THOP1 levels were measured in a selection of CSF samples from the original discovery study (24 cognitively unimpaired controls, 24 AD and 24 dementia with Lewy bodies (DLB) patients) on both platforms. Passing bablok‐regression analysis showed that platforms were comparable and thus we selected the Ella platform for further validation in two independent cohorts (Amsterdam Dementia cohort (ADC) including 56 controls, 45 mild cognitive impairment with amyloid pathology (MCI‐Aβ+), 55 AD and 55 DLB patients; Sant Pau Initiative on Neurodegeneration (SPIN) cohort including 53 controls, 50 MCI‐Aβ+, 47 AD and 50 DLB patients). Differences in THOP1 levels between diagnostic groups and correlations to the AD CSF biomarkers were tested by ANCOVA (corrected for sex) and Spearman correlation analyses.ResultTHOP1 levels moderately correlated between proteomics analysis and the novel assays (Olink‐Ella: rho=0.720; Olink‐Simoa: rho=0.584; Ella‐Simoa rho=0.713; figure 1). Consistent with results obtained on the Olink proteomics platform, THOP1 levels were significantly increased in AD compared to DLB patients (Ella: 1.6‐fold; Simoa: 1.7‐fold) and controls (Ella: 1.8‐fold; Simoa: p>0.05). We next showed increased THOP1 levels in both MCI‐Aβ+ (ADC: 1.2‐fold, p<0.05; SPIN: 1.4‐fold, p<0.001) and AD patients (ADC: 1.03‐fold, p>0.05; SPIN: 1.4‐fold, p<0.001) compared to controls (Figure 2). THOP1 levels correlated moderately with levels of t‐Tau (ADC: rho=0.644; SPIN: rho=0.721), p‐Tau (ADC: rho=0.480; SPIN: rho=0.712) and Aβ40 (ADC: not available; SPIN: rho=0.625) but not with Aβ42 (ADC: rho=0.110; SPIN: rho=‐0.09).ConclusionWe show increased CSF THOP1 levels in amyloid positive AD and MCI patients and correlation with CSF Aβ40, which together with literature showing colocalization of THOP1 with Aβ plaques suggests a relationship with Aβ‐production. The association of THOP1 to tau pathologies highlights the potential of this protein as a disease monitoring biomarker.

An Efficient Architecture and High-Throughput Implementation of CCSDS-123.0-B-2 Hybrid Entropy Coder Targeting Space-Grade SRAM FPGA Technology

Nowadays, hyperspectral imaging is recognized as cornerstone remote sensing technology. The explosive growth in image data volume and instrument data rates, compete with limited on-board storage resources and downlink bandwidth, making hyperspectral image data compression a mission critical on-board processing task. The Consultative Committee for Space Data Systems (CCSDS) extended the previous issue of the CCSDS-123.0 Recommended Standard for multi- and hyperspectral image compression to provide with Near-Lossless compression functionality. A key feature of the CCSDS-123.0-B-2 is the improved Hybrid Entropy Coder, which at low bit rates, provides substantially better compression performance than the Issue 1 entropy coders. In this paper, we introduce a high-throughput hardware implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder. The introduced architecture exploits the systolic design pattern to provide modularity and latency insensitivity in a deep and elastic pipeline achieving a constant throughput of 1 sample/cycle with a small FPGA resource footprint. This architecture is described in portable VHDL RTL and is implemented, validated and demonstrated on a commercially available Xilinx KCU105 development board hosting a Xilinx Kintex Ultrascale XCKU040 SRAM FPGA, and thus, is directly transferable to Xilinx Radiation Tolerant Kintex UltraScale XQRKU060 space-grade devices for space deployments. Moreover, state-of-the-art SpaceFibre (ECSS-E-ST-50-11C) serial link interface and test equipment were used in the validation platform to emulate an on-board deployment. The introduced CCSDS-123.0-B-2 Hybrid Entropy Encoder achieves a constant throughput performance of 305 MSamples/s. To the best of our knowledge, this is the first published fully-compliant architecture and high-throughput implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder, targeting space-grade FPGA technology.

VeVaPy, a Python Platform for Efficient Verification and Validation of Systems Biology Models with Demonstrations Using Hypothalamic-Pituitary-Adrenal Axis Models.

In order for mathematical models to make credible contributions, it is essential for them to be verified and validated. Currently, verification and validation (V&V) of these models does not meet the expectations of the system biology and systems pharmacology communities. Partially as a result of this shortfall, systemic V&V of existing models currently requires a lot of time and effort. In order to facilitate systemic V&V of chosen hypothalamic-pituitary-adrenal (HPA) axis models, we have developed a computational framework named VeVaPy-taking care to follow the recommended best practices regarding the development of mathematical models. VeVaPy includes four functional modules coded in Python, and the source code is publicly available. We demonstrate that VeVaPy can help us efficiently verify and validate the five HPA axis models we have chosen. Supplied with new and independent data, VeVaPy outputs objective V&V benchmarks for each model. We believe that VeVaPy will help future researchers with basic modeling and programming experience to efficiently verify and validate mathematical models from the fields of systems biology and systems pharmacology.

Biomimetic design of bioartificial scaffolds for the in vitro modelling of human cardiac fibrosis.

In vitro models of pathological cardiac tissue have attracted interest as predictive platforms for preclinical validation of therapies. However, models reproducing specific pathological features, such as cardiac fibrosis size (i.e., thickness and width) and stage of development are missing. This research was aimed at engineering 2D and 3D models of early-stage post-infarct fibrotic tissue (i.e., characterized by non-aligned tissue organization) on bioartificial scaffolds with biomimetic composition, design, and surface stiffness. 2D scaffolds with random nanofibrous structure and 3D scaffolds with 150µm square-meshed architecture were fabricated from polycaprolactone, surface-grafted with gelatin by mussel-inspired approach and coated with cardiac extracellular matrix (ECM) by 3weeks culture of human cardiac fibroblasts. Scaffold physicochemical properties were thoroughly investigated. AFM analysis of scaffolds in wet state, before cell culture, confirmed their close surface stiffness to human cardiac fibrotic tissue. Following 3weeks culture, biomimetic biophysical and biochemical scaffold properties triggered the activation of myofibroblast phenotype. Upon decellularization, immunostaining, SEM and two-photon excitation fluorescence microscopy showed homogeneous decoration of both 2D and 3D scaffolds with cardiac ECM. The versatility of the approach was demonstrated by culturing ventricular or atrial cardiac fibroblasts on scaffolds, thus suggesting the possibility to use the same scaffold platforms to model both ventricular and atrial cardiac fibrosis. In the future, herein developed in vitro models of cardiac fibrotic tissue, reproducing specific pathological features, will be exploited for a fine preclinical tuning of therapies.

Edge Intelligent Joint Optimization for Lifetime and Latency in Large-Scale Cyber–Physical Systems

In recent years, the exploration on large-scale cyber–physical systems (CPSs) has become a fertile research field of significant impact. Large-scale CPS applications cover not only manufacturing and production areas but also daily living domains. Traditional solutions dedicated for large-scale CPSs mainly concentrate on the service latency or reliability optimization, but neglect the resultant negative impact on system lifetime. In this article, we conduct the first study on jointly optimizing the service latency and system lifetime subject to the constraints of reliability, energy consumption, and schedulability for large-scale CPSs. We propose an edge intelligent solution composed of offline and online phases. At the offline phase, the long short-term memory (LSTM) technique is leveraged to predict task offloading rates at individual user groups. Afterward, the multiobjective evolutionary algorithm with dual local search (DLS-MOEA) is exploited to determine optimal system static settings of computation offloading mapping and task replication number. At the online phase, an affinity-driven scheme incurring minimal system dynamic overheads is designed to deal with the inherent mobility of terminal users. We also build an algorithm validation platform upon which extensive simulation experiments are carried out. Experimental results show that our offline and online schemes outperform the state-of-the-art benchmarking methods by 27.1% and 43.5%, respectively.

Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control.

Recent Progress and Challenges in Microscale Urban Heat Modeling and Measurement for Urban Engineering Applications

AbstractThis review focuses on progress and emerging challenges in experimentally validated modeling of microscale urban thermal environments over the last two decades. In the last few decades, there has been a surge in urban energy contribution resulting in elevated urban day-/night-time air temperatures. While there is no single solution to urban heat, mitigation strategies can be implemented to minimize the harmful effects of urban heat both on humans and the environment. To study the effects of urban heat, numerical modeling of urban thermal environments has seen a rise in usage of several application specific atmospheric modeling software packages, and multiple studies and reviews have already covered the prolific engineering use cases. However, there are inherent and unintentional biases introduced by each modeling software package, that inhibit validity and accuracy for general engineering use. This review critically analyzes the limitations of current state-of-the-art (SOA) microscale atmospheric modeling approaches and identify necessary areas for improvement. Urban thermal environment models must be validated with measurements to gain confidence in the predictive capabilities. This review will additionally examine the next generation of measurement techniques that leverage advances in computing and communications to create distributed meteorological sensor networks for improved spatial and temporal resolutions, that can provide a rich platform for model validation. High fidelity and accurate simulations of urban thermal environments improve confidence in the study of urban heat, its mitigation, and its impact on urban engineering applications in building energy usage and sustainability.

Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning

Because of the high-power demand of fuel cell hybrid vehicles, a multi-stack fuel cell system (MFCS) composed of multiple low-power fuel cell stacks (FCSs) instead of a high-power one has become a satisfactory solution. This is due to the modularity of MFCS, which is more reliable and durable. However, the hybrid power system (MHPSS) of an MFCS hybrid electric vehicle possesses not only MFCS, but also batteries to improve the dynamic performance of MHPSS. Due to the difference in characteristics of MFCS and battery, the energy management strategy (EMS) of MHPSS is the key of ensuring its safe and efficient operation. However, most of the existing MHPSS EMSs are complicated in design and complex to compute online. Besides, they also do not consider the robustness of EMS, that is, EMS has the ability to guarantee the safe operation of MHPSS when the MFCS fails. To solve the above problems, this paper proposes an EMS based on independent Q-learning (IQL) which is an algorithm of multi-agent reinforcement learning to maintain battery state of charge (SOC) and minimize hydrogen consumption. The proposed EMS is not only simple in design, but also can guarantee the normal operation of the MHPSS when the MFCS fails, and can be transplanted to execute online on a microcontroller unit or a field programmable gate array. The various parts of the MHPSS model are first built, then the IQL strategy (IQLS) is trained offline in the established model environment, and finally, the IQLS is ported to the hardware-in-the-loop platform for validation. In order to verify the effectiveness of the proposed EMS, the solution of dimensionality-reduced DP is also used to calculate the fuel economy. Through the experimental verification under different initial SOC and driving cycles, it can be seen that the IQLS proposed in this paper can achieve the goal of maintaining battery SOC and minimizing hydrogen consumption, and also has good generalization ability and safe operation ability under fault conditions.

Modelling of the Dynamically Positioned vessels and managed ice-field interaction dynamics using empirical-statistical techniques

Empirical models based on measurements are often preferred over high-fidelity numerical models when real-time predictions of broken ice-field actions on ships and floating offshore platforms are needed. This paper presents the methodologies and validations of a novel numerical model based on empirical-statistical techniques for predicting dynamically positioned (DP) vessels and ice-field interactions. The authors developed multiple regression models for predicting the time-averaged and average peaks of thruster forces and yawing moment of the DP vessel due to managed ice actions. Monte Carlo Simulations and other empirical techniques were then developed to predict the time series of the ice forces and moment on the DP vessel in managed ice scenarios. The authors then carried out model validations for several ice-field and vessel operating scenarios. The model showed reasonable accuracy in predicting the effects of several ice-field parameters on the forces and moments of two DP-controlled vessels. Subsequently, integration of the model into a DP-in-ice validation platform is offered, which can simulate and optimize the performance of a DP-controlled vessel in ice-infested water. Finally, the uniqueness and limitations of the models are discussed, and recommendations for future work are provided.

Establishment of Quantitative Proteomics Platform for Urine Biomarker Discovery and Validation

Establishment of Quantitative Proteomics Platform for Urine Biomarker Discovery and Validation

A reconstituted depolarization-induced Ca2+ release platform for validation of skeletal muscle disease mutations and drug discovery.

In skeletal muscle excitation-contraction (E-C) coupling, depolarization of the plasma membrane triggers Ca2+ release from the sarcoplasmic reticulum (SR), referred to as depolarization-induced Ca2+ release (DICR). DICR occurs through the type 1 ryanodine receptor (RyR1), which physically interacts with the dihydropyridine receptor Cav1.1 subunit in specific machinery formed with additional essential components including β1a, Stac3 adaptor protein, and junctophilins. Exome sequencing has accelerated the discovery of many novel mutations in genes encoding DICR machinery in various skeletal muscle diseases. However, functional validation is time-consuming because it must be performed in a skeletal muscle environment. In this study, we established a platform of the reconstituted DICR in HEK293 cells. The essential components were effectively transduced into HEK293 cells expressing RyR1 using baculovirus vectors, and Ca2+ release was quantitatively measured with R-CEPIA1er, a fluorescent ER Ca2+ indicator, without contaminant of extracellular Ca2+ influx. In these cells, [K+]-dependent Ca2+ release was triggered by chemical depolarization with the aid of inward rectifying potassium channel, indicating a successful reconstitution of DICR. Using the platform, we evaluated several Cav1.1 mutations that are implicated in malignant hyperthermia and myopathy. We also tested several RyR1 inhibitors; whereas dantrolene and Cpd1 inhibited DICR, procaine had no effect. Furthermore, twitch potentiators such as perchlorate and thiocyanate shifted the voltage dependence of DICR to more negative potentials without affecting Ca2+-induced Ca2+ release. These results well reproduced the findings with the muscle fibers and the cultured myotubes. Since the procedure is simple and reproducible, the reconstituted DICR platform will be highly useful for the validation of mutations and drug discovery for skeletal muscle diseases.

Drosophila Nephrocytes as a Personalized Platform for Validation of Variants in TBC1D8B-Associated Focal Segmental Glomerulosclerosis

Drosophila Nephrocytes as a Personalized Platform for Validation of Variants in TBC1D8B-Associated Focal Segmental Glomerulosclerosis

Multi-Institutional Validation of a Smartphone-Based Surface Guided Radiation Therapy (SGRT) Tracking System for Respiratory Monitoring in Radiation Therapy

<h3>Purpose/Objective(s)</h3> Surface guidance radiation therapy (SGRT) systems are becoming standard-of-care for patient setup and motion monitoring. However, commercial systems remain inaccessible to resource-limited clinics around the world. The iOS Surface Audiovisual Biofeedback (iSAVB) system implements ubiquitous mobile technology as a versatile tool for surface imaging in resource-strapped clinics. The purpose of this study is to establish a robust validation platform, commission iSAVB at multiple participating institutions, and validate iSAVB using phantom motion and against commercial systems during patient treatments. <h3>Materials/Methods</h3> Three participating institutions are implementing, validating, and comparing the iSAVB system against current clinical SGRT systems. The iSAVB system consists of depth camera enabled iOS device, to acquire real-time point-cloud data for surface rendering, a 3D printed stand capable of mounting and indexing on a treatment couch, and aa tablet (or external workstation) for audio-visual feedback and signal processing. Validation of the iSAVB system was performed against commercial optical systems at each center using a respiratory motion phantom. Motion traces were analyzed using linear regressions and Bland-Altman analysis. <h3>Results</h3> The iSAVB system was successfully installed at an initial institution and tested on phantoms and healthy volunteers. For free-breathing traces, iSAVB was significantly correlated to phantom input motion (r=0.99, p<0.0001; bias=0.00±0.05cm, 95%CI=−0.1cm,0.1cm) and commercial system motion (r=0.99, p<0.0001; bias=0.00±0.07cm, 95%CI=−0.14cm,0.14cm). Systems are currently being installed at the two additional institutions that will perform similar commissioning and validation. <h3>Conclusion</h3> A multi-center validation shows the iSAVB system can be used for surface motion monitoring and provides SGRT capabilities similar to current high-end clinical systems. The iSAVB system can be used for 3D/IMRT/VMAT treatments, although future validation is required for SBRT treatments. Future work includes establishing reliable commissioning protocols that follow TG-302 guidelines for resource-strapped departments to benefit from affordable and accurate surface imaging guidance. Reference:

How to Digitize and Permanently Preserve Planning Documentation: The Collection of Plans of the Architectural Department of the Ministry of Construction of the Kingdom of Yugoslavia (Preconditions, Priorities, Criteria, Primary and Final Goals and New Technologies)

Digitization of large format archives, in this case architectural plans, is a great challenge, not only in technical terms due to their dimensions and the nature and sensitivity of the materials, but also because of the choice of digitization equipment. As a problem of particular importance, the harmonization of archival standards is also imposed as necessary as well as the information standards, the creation of valid digital platforms and the standards prescribed in the formation of the content. Based on modern examples of practice, the study will address and explain the improved analogue technology in the form of microfilming, as well as the possibilities that it offers in the field of protection and conservation of urban documentation today in the form of manipulation of the digital record of information with analogue technologies and their mutual relationship. An example of this will be given through the presentation of the Piql program and its role in storing data that strive for authenticity and durability on media that provide the security without the possibility of losing the metadata within the network and energy consumption. The presentation will briefly show the state of analogue technologies in archival practice in Serbia and the perspectives of their future technological improvement.

In situ quantification of temperature and strain within photovoltaic modules through optical sensing

AbstractThe ability to quantify internal strain levels within a photovoltaic (PV) laminate is essential to aid in the development of reliable and sustainable PV modules. This need is even greater for emerging applications with a high degree of integration such as vehicle and infrastructure integrated PV. Within this work, we demonstrate a scalable optical sensing solution, which allows for the in situ thermo‐mechanical strain and temperature monitoring of photovoltaic modules. Using a combination of two optic fibers with fiber Bragg gratings (FBGs) with a different packaging, an absolute in‐laminate temperature accuracy of 0.3°C has been achieved along with the ability to detect changes in strain as low as 0.248 μϵ. The sensor solution provides the potential to monitor and quantify various failure modes or degradation at various stages in the development process up to in‐field monitoring. Furthermore, it provides a platform for the direct validation of physics‐based simulations.

A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model.

There is growing evidence for the key role of microglial functional state in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools Aiforia® Cloud (Aifoira Inc., Cambridge, MA, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinson’s disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are available within the Aiforia® platform for study-specific adaptation and validation.

Theoretical innovation of component-based Chinese medicine and its exemplary practice: the study on creating Guanxinning Tablets

Upholding the wisdom of traditional Chinese medicine that the therapeutic principle, method, formula and medicine are coherent with each other, we propose the technical methodology for intelligent creation of component-based Chinese medicine by integrating multidisciplinary knowledge such as artificial intelligence, pharmaceutical informatics, system pharmacology and phytochemistry. Taking the creation of Guanxinning Tablets as an example, we expound the technical principle for creating component-based Chinese medicine and briefly describe the design method for optimizing the entity of Chinese medicine efficacy by rational combination of active components. Our research sought to &quot;clarify and explain&quot; the mechanism of its clinical treatment action through multi-modal and multi-scale systematic pharmacology studies. This work emphatically demonstrates the pilot workshop and engineering validation platform based on the intelligent simulation of whole production process, and outlines the design principles of the intelligent production line for innovative Chinese medicine. The results of industrial research show that the ourself established method for evaluating the process quality controllability and intelligent production line can be applied to manufacturing Guanxining Tablets with high quality. Through the innovative research of multidisciplinary cross-border integration, the present work explored a new way for the creation of modern Chinese medicine.

Keystone Malaria Symposium 2022: a vibrant discussion of progress made and challenges ahead from drug discovery to treatment

Keystone Malaria Symposium 2022: a vibrant discussion of progress made and challenges ahead from drug discovery to treatment

Abstract 2812: CSPG4-specific CAR.CIK lymphocyte-based immunotherapy to eliminate HLA class I-defective melanoma tumors

Abstract Purpose of this study is to explore the preclinical efficacy of Chondroitin Sulfate Proteoglycan 4 (CSPG4)-specific CAR-engineered Cytokine-Induced Killer lymphocytes (CIK) to eradicate melanoma cells with defective HLA class I (HLA-I). The latter abnormality plays a major role in clinical resistance to Checkpoint Inhibitors (CI). CSPG4 was selected as the target because of its high expression on melanoma and its restricted distribution in normal tissues. Our approach is based on CAR.CIK redirected against CSPG4. CIK, ex vivo expanded T-NK lymphocytes endowed with intrinsic HLA-independent antitumor activity, were used as effectors. Experimental procedure. CAR.CIK were generated by retroviral transduction of patient derived PBMC with a vector encoding a 2nd generation CSPG4-CAR with 4-1BB co-stimulation. Surface HLA-I expression was evaluated by flow cytometry on melanoma cell lines (Mel), derived by surgical biopsies. These cells also served as targets for CSPG4 CAR.CIK. The activity of CSPG4 CAR.CIK was analyzed in vitro and in immunodeficient mice grafted with a patient-derived HLA-defective melanoma. Mice were treated intravenously with 3x106 CAR.CIK (5 cells infusions in total). Results. CAR.CIK were efficiently generated from 4 melanoma patients. Mean expression of CSPG4-CAR was 48±8%, the rate of ex vivo expansion (84 fold) and phenotypic characteristics (CD3+CD8+=71±13%, CD3+ CD56+=22.5±13%, NKG2D+= 68±32.3%) were comparable with unmodified controls. Membrane HLA-I molecules were detected in 23/24 Mel samples, with a variable membrane density per cell (median 21232, range 787-49871). Sample Mel17 did not express HLA-I because of a start lost mutation (p.Met1Ile) in β2microglobulin. CSPG4 was intensely expressed by all Mel (78±5%), with no correlation to HLA-I levels. CSPG4 CAR.CIK efficiently killed 10 Mel samples in vitro, including Mel17 (HLA-I negative) and 2 Mel with the lowest HLA-I density (Mel71, Mel72). Mean Mel-specific killing by CSPG4 CAR.CIK was significantly higher compared with unmodified CIK especially at low effector/target (E/T) ratios (85% vs 40% at E/T=1:1; 46% vs 9% at E/T=1:8, p&amp;lt;0.0001). In vivo, CSPG4 CAR.CIK caused a significant inhibition of the HLA-negative Mel17 tumor growth (p&amp;lt;0.0001) as compared to controls. Antitumor activity was confirmed by the reduction of tumor weight and metabolic activity (by fluorescent glucose uptake) measured on the residual explanted tumors. Antitumor response persisted up to 2 weeks after end of the treatment. Conclusions. We reported the activity of CSPG4 CAR.CIK against melanoma, including those with low or defective HLA-I expression. CIK may provide a valid platform for CAR-based strategies against melanoma and solid tumors in general. Our data provide the rationale to implement clinical studies exploring the proposed strategy in melanoma patients not responding or relapsing after immunotherapy with CI. Citation Format: Giulia Cattaneo, Lidia Giraudo, Loretta Gammaitoni, Ilenia Iaia, Fabrizio Carnevale-Schianca, Alberto Pisacane, Enrico Berrino, Caterina Marchiò, Luca Paruzzo, Andrea Michela Biolato, Chiara Donini, Marco Basiricò, Elisa Landoni, Soldano Ferrone, Valeria Leuci, Massimo Aglietta, Gianpietro Dotti, Dario Sangiolo. CSPG4-specific CAR.CIK lymphocyte-based immunotherapy to eliminate HLA class I-defective melanoma tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2812.