MLL1 regulates cytokine-driven cell migration and metastasis.

Cell migration is a critical contributor to metastasis. Cytokine production and its role in cancer cell migration have been traditionally associated with immune cells. We find that the histone methyltransferase Mixed-Lineage Leukemia 1 (MLL1) controls 3D cell migration via cytokines, IL-6, IL-8, and TGF-ß1, secreted by the cancer cells themselves. MLL1, with its scaffold protein Menin, controls actin filament assembly via the IL-6/8/pSTAT3/Arp3 axis and myosin contractility via the TGF-ß1/Gli2/ROCK1/2/pMLC2 axis, which together regulate dynamic protrusion generation and 3D cell migration. MLL1 also regulates cell proliferation via mitosis-based and cell cycle-related pathways. Mice bearing orthotopic MLL1-depleted tumors exhibit decreased lung metastatic burden and longer survival. MLL1 depletion leads to lower metastatic burden even when controlling for the difference in primary tumor growth rates. Combining MLL1-Menin inhibitor with paclitaxel abrogates tumor growth and metastasis, including preexistent metastasis. These results establish MLL1 as a potent regulator of cell migration and highlight the potential of targeting MLL1 in patients with metastatic disease.

Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors.

Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNFα, IFNγ, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines' cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors.

Formulating Electron Beam-Induced Covalent Linkages for Stable and High-Energy-Density Silicon Microparticle Anode.

High-capacity silicon (Si) materials hold a position at the forefront of advanced lithium-ion batteries. The inherent potential offers considerable advantages for substantially increasing the energy density in batteries, capable of maximizing the benefit by changing the paradigm from nano- to micron-sized Si particles. Nevertheless, intrinsic structural instability remains a significant barrier to its practical application, especially for larger Si particles. Here, a covalently interconnected system is reported employing Si microparticles (5 µm) and a highly elastic gel polymer electrolyte (GPE) through electron beam irradiation. The integrated system mitigates the substantial volumetric expansion of pure Si, enhancing overall stability, while accelerating charge carrier kinetics due to the high ionic conductivity. Through the cost-effective but practical approach of electron beam technology, the resulting 500 mAh-pouch cell showed exceptional stability and high gravimetric/volumetric energy densities of 413 Wh kg-1, 1022 Wh L-1, highlighting the feasibility even in current battery production lines.

Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.

In neurointensive care units (NICU), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of pre-admission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Effect of α″-Ti Martensitic Phase Formation on Plasticity in Ti-Fe-Sn Ultrafine Eutectic Composites.

Extensive research has been conducted on Ti-Fe-Sn ultrafine eutectic composites due to their high yield strength, compared to conventional microcrystalline alloys. The unique microstructure of ultrafine eutectic composites, which consists of the ultrafine-grained lamella matrix with the formation of primary dendrites, leads to high strength and desirable plasticity. A lamellar structure is known for its high strength with limited plasticity, owing to its interface-strengthening effect. Thus, extensive efforts have been conducted to induce the lamellar structure and control the volume fraction of primary dendrites to enhance plasticity by tailoring the compositions. In this study, however, it was found that not only the volume fraction of primary dendrites but also the morphology of dendrites constitute key factors in inducing excellent ductility. We selected three compositions of Ti-Fe-Sn ultrafine eutectic composites, considering the distinct volume fractions and morphologies of ß-Ti dendrites based on the Ti-Fe-Sn ternary phase diagram. As these compositions approach quasi-peritectic reaction points, the α″-Ti martensitic phase forms within the primary ß-Ti dendrites due to under-cooling effects. This pre-formation of the α″-Ti martensitic phase effectively governs the growth direction of ß-Ti dendrites, resulting in the development of round-shaped primary dendrites during the quenching process. These microstructural evolutions of ß-Ti dendrites, in turn, lead to an improvement in ductility without a significant compromise in strength. Hence, we propose that fine-tuning the composition to control the primary dendrite morphology can be a highly effective alloy design strategy, enabling the attainment of greater macroscopic plasticity without the typical ductility and strength trade-off.

Differentiating changes in movement-related EEG response induced by transcranial direct current stimulation using convolutional neural network.

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that can modulate neuronal excitability and induce brain plasticity. Although tDCS has been studied with various methods, more research is needed on the movement-related electroencephalography (EEG) changes induced by tDCS. Moreover, it is necessary to investigate whether these changes can be distinguished through a convolutional neural network (CNN)-based classifier. In this study, we measured the EEG during the voluntary foot-tapping task of participants who received tDCS or sham stimulation and evaluated the classification performance. As a result, significantly higher classification accuracy was shown using the ß band (88.7±9.4%), which is more related to motor function, than in the other bands (71.4±10.6% for δ band, 64.1±13.4% for θ band, and 65.7±10.9% for α band). Consequently, EEG changes during the voluntary foot-tapping task induced by tDCS appeared large in the ß band, implying that it is effective in classifying whether tDCS was given or not, and plays an important role in identifying the effect of tDCS.

Impact of Memory T Cells on SARS-COV-2 Vaccine Response in Hematopoietic Stem Cell Transplant.

During the COVID-19 pandemic, hematopoietic stem cell transplant (HSCT) recipients faced an elevated mortality rate from SARS-CoV-2 infection, ranging between 10-40%. The SARS-CoV-2 mRNA vaccines are important tools in preventing severe disease, yet their efficacy in the post-transplant setting remains unclear, especially in patients subjected to myeloablative chemotherapy and immunosuppression. We evaluated the humoral and adaptive immune responses to the SARS-CoV-2 mRNA vaccination series in 42 HSCT recipients and 5 healthy controls. Peripheral blood mononuclear nuclear cells and serum were prospectively collected before and after each dose of the SARS-CoV-2 vaccine. Post-vaccination responses were assessed by measuring anti-spike IgG and nucleocapsid titers, and antigen specific T cell activity, before and after vaccination. In order to examine mechanisms behind a lack of response, pre-and post-vaccine samples were selected based on humoral and cellular responses for single-cell RNA sequencing with TCR and BCR sequencing. Our observations revealed that while all participants eventually mounted a humoral response, transplant recipients had defects in memory T cell populations that were associated with an absence of T cell response, some of which could be detected pre-vaccination.

Design of High-Performance Organic Nonlinear Optical and Terahertz Crystals by Controlling the van der Waals Volume.

In the development of new organic crystals for nonlinear optical and terahertz (THz) applications, it is very challenging to achieve the essentially required non-centrosymmetric molecular arrangement. Moreover, the resulting crystal structure is mostly unpredictable due to highly dipolar molecular components with complex functional substituents. In this work, new organic salt crystals with top-level macroscopic optical nonlinearity by controlling the van der Waals volume (VvdW ), rather than by trial and error, are logically designed. When the VvdW of molecular ionic components varies, the corresponding crystal symmetry shows an observable trend: change from centrosymmetric to non-centrosymmetric and back to centrosymmetric. All non-centrosymmetric crystals exhibit an isomorphic P1 crystal structure with an excellent macroscopic second-order nonlinear optical response. Apart from the top-level macroscopic optical nonlinearity, new organic crystals introducing highly electronegative fluorinated substituents with strong secondary bonding ability show excellent performance in efficient and broadband THz wave generation, high crystal density, high thermal stability, and good bulk crystal growth ability.

Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients.

Motor imagery (MI)-based brain-computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and sensorimotor cortex during MI are similar to those of motor execution/imagery. However, individuals paralyzed owing to various neurological disorders have debilitated activation of the motor control region. Therefore, the differences in brain activation based on the paralysis location should be considered. We analyzed brain activation patterns using the electroencephalogram (EEG) acquired while performing MI on the right upper limb to investigate hemiplegia-related brain activation patterns. Participants with hemiplegia of the right upper limb (n=7) and left upper limb (n=4) performed the MI task within the right upper limb. EEG signals were acquired using 14 channels based on a 10-20 global system, and analyzed for event-related desynchronization (ERD) based on event-related spectral perturbation and functional connectivity, using the weighted phase-lag index of both hemispheres at the location of hemiplegia. Enhanced ERD was found in the ipsilateral region, compared to the contralateral region, after MI of the affected limb. The reduced difference in the centrality of the channels was observed in all subjects, likely reflecting an altered brain network from increased interhemispheric connections. Furthermore, the tendency of distinct network-based features depending on the MI task on the affected limb was diluted between the inter-hemispheres. Analysis of interaction between inter-region using functional connectivity could provide avenues for further investigation of BCI strategy through the brain state of individuals with hemiplegia.

Prior cycles of anti-CD20 antibodies affect antibody responses after repeated SARS-CoV-2 mRNA vaccination.

BACKGROUNDWhile B cell depletion is associated with attenuated antibody responses to SARS-CoV-2 mRNA vaccination, responses vary among individuals. Thus, elucidating the factors that affect immune responses after repeated vaccination is an important clinical need.METHODSWe evaluated the quality and magnitude of the T cell, B cell, antibody, and cytokine responses to a third dose of BNT162b2 or mRNA-1273 mRNA vaccine in patients with B cell depletion.RESULTSIn contrast with control individuals (n = 10), most patients on anti-CD20 therapy (n = 48) did not demonstrate an increase in spike-specific B cells or antibodies after a third dose of vaccine. A third vaccine elicited significantly increased frequencies of spike-specific non-naive T cells. A small subset of B cell-depleted individuals effectively produced spike-specific antibodies, and logistic regression models identified time since last anti-CD20 treatment and lower cumulative exposure to anti-CD20 mAbs as predictors of those having a serologic response. B cell-depleted patients who mounted an antibody response to 3 vaccine doses had persistent humoral immunity 6 months later.CONCLUSIONThese results demonstrate that serial vaccination strategies can be effective for a subset of B cell-depleted patients.FUNDINGThe NIH (R25 NS079193, P01 AI073748, U24 AI11867, R01 AI22220, UM 1HG009390, P01 AI039671, P50 CA121974, R01 CA227473, U01CA260507, 75N93019C00065, K24 AG042489), NIH HIPC Consortium (U19 AI089992), the National Multiple Sclerosis Society (CA 1061-A-18, RG-1802-30153), the Nancy Taylor Foundation for Chronic Diseases, Erase MS, and the Claude D. Pepper Older Americans Independence Center at Yale (P30 AG21342).

Investigation of enterprise economic management model based on fuzzy logic algorithm.

With the development of modern society and economy, modern business models have changed. Enterprise management is no longer the main function, which requires new management concepts to coordinate production and operation and create new management models. Corporate economic management is one of the main tasks of corporate governance. In order to improve corporate governance, the effective corporate economic evaluation model must be established. The advantage of fuzzy logic algorithm is that it can effectively express fuzzy data and is easy to understand, which is more transparent. The application of fuzzy logic algorithm in the research of enterprise economic management can fully reflect the advantages of fuzzy logic algorithm and improve enterprise governance. In this paper, the fuzzy logic algorithm was used to study the economic management model of enterprises. This paper first introduced the enterprise economic management model, including the definition, characteristics and shortcomings of the enterprise economic management model. The performance evaluation methods of enterprise economic management were introduced, including the clarity of performance evaluation objectives, the optimization of performance evaluation means and the feedback of performance evaluation results. Finally, the fuzzy logic algorithm was used to evaluate the performance of enterprise economic management, and the indicators and performance evaluation model of enterprise economic management were established. In the experimental part, the fuzzy logic algorithm was used to evaluate the economic management of four enterprises. The experimental results showed that Enterprise A had the highest ability to evaluate the economic management of enterprises, and the comprehensive evaluation result of economic management was above 0.9, so it had a very high enterprise economic management ability. The application of fuzzy logic to the study of enterprise economic management could directly show the advantages and disadvantages of the enterprise economic management model through quantitative analysis of the enterprise economic management model. This enabled enterprises to understand the current economic management situation and improve it based on the facts, which helped enterprises to increase their economic management ability.

Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis.

We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.

Microfluidic Immuno-Serolomic Assay Reveals Systems Level Association with COVID-19 Pathology and Vaccine Protection.

How to develop highly informative serology assays to evaluate the quality of immune protection against coronavirus disease-19 (COVID-19) has been a global pursuit over the past years. Here, a microfluidic high-plex immuno-serolomic assay is developed to simultaneously measure50 plasma or serum samples for50 soluble markers including 35proteins, 11 anti-spike/receptor binding domian (RBD) IgG antibodies spanningmajor variants, and controls. This assay demonstrates the quintuplicate test in a single run with high throughput, low sample volume, high reproducibilityand accuracy. It is applied to the measurement of 1012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein analysis reveals distinct immune mediator modules that exhibit a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies or receiving B cell depletion therapy. Serological analysis identifies that COVID-infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which can be associated with limited clonotype diversity and functional deficiency in B cells. These findings underscore the importance to individualize immunization strategies for these high-risk patients and provide an informative tool to monitor their responses at the systems level.

Molecularly Imprinted Chemiresistive Sensor for Specific Recognition of Furaneol as a Biomarker of Strawberry Flavor Conditions.

This work introduces the concept of a molecularly imprinted gas sensor to monitor the condition of naturally ripened strawberries. Furaneol, 2,5-dimethyl-4-hydroxy-3(2H)-furanone, is considered as an important biomarker related to the strawberry flavor. Identification of furaneol concentration is still a challenge because of its weak adsorption, nonpolar, and unreactive properties. Therefore, no study has been reported yet to measure furaneol gases via a simple chemiresistive mechanism. Herein, we demonstrate the sensor based on molecularly imprinted polymer (MIP)-based polyaniline (PANI). The sensitive and selective detection of furaneol gas with a MIP-PANI gas sensor was observed at room temperature and under different humidity conditions. The comparison between MIP and the nonimprinted (NIP)-based PANI shows a strong interaction between furaneol and the molecularly imprinted polymer. The furaneol gas sensing mechanism is explained based on the interaction between the gas molecules and the charge carriers of MIP-PANI, which results in the functional group change in the carboxylic group. Furthermore, the developed MIP-chemiresistive sensor for real strawberries was compared with a commercial e-nose system. The results show the potential to offer a rapid and cost-effective platform for specific recognition of furaneol.

Prediction of Serious Intracranial Hypertension from Low-Resolution Neuromonitoring in Traumatic Brain Injury: An Explainable Machine Learning Approach.

There is a strong association between intracranial hypertension (IH) that occurs following the acute phase of traumatic brain injury (TBI) and negative outcomes. This study proposes a pressure-time dose (PTD)-based parameter that may specify a possible serious IH (SIH) event and develops a model to predict SIH. The minute-by-minute signals of arterial blood pressure (ABP) and intracranial pressure (ICP) of 117 TBI patients were utilized as the internal validation dataset. The SIH event was explored through the prognostic power of the IH event variables for the outcome after 6 months, and an IH event with thresholds that included an ICP of 20 mmHg and PTD > 130 mmHg * minutes was considered an SIH event. The physiological characteristics of normal, IH and SIH events were investigated. LightGBM was employed to forecast an SIH event from various time intervals using physiological parameters derived from the ABP and ICP. Training and validation were conducted on 1,921 SIH events. External validation was performed on two multi-center datasets containing 26 and 382 SIH events. The SIH parameters could be used to predict mortality (AUROC = 0.893, p < 0.001) and favorability (AUROC = 0.858, p < 0.001). The trained model robustly forecasted SIH after 5 and 480 minutes with an accuracy of 86.95% and 72.18% in internal validation. External validation also revealed a similar performance. This study demonstrated that the proposed SIH prediction model has reasonable predictive capacities. A future intervention study is required to investigate whether the definition of SIH is maintained in multi-center data and to ensure the effects of the predictive system on TBI patient outcomes at the bedside.

CXCL8/CXCR2 signaling mediates bone marrow fibrosis and is a therapeutic target in myelofibrosis.

Proinflammatory signaling is a hallmark feature of human cancer, including in myeloproliferative neoplasms (MPNs), most notably myelofibrosis (MF). Dysregulated inflammatory signaling contributes to fibrotic progression in MF; however, the individual cytokine mediators elicited by malignant MPN cells to promote collagen-producing fibrosis and disease evolution are yet to be fully elucidated. Previously, we identified a critical role for combined constitutive JAK/STAT and aberrant NF-κB proinflammatory signaling in MF development. Using single-cell transcriptional and cytokine-secretion studies of primary cells from patients with MF and the human MPLW515L (hMPLW515L) murine model of MF, we extend our previous work and delineate the role of CXCL8/CXCR2 signaling in MF pathogenesis and bone marrow fibrosis progression. Hematopoietic stem/progenitor cells from patients with MF are enriched for a CXCL8/CXCR2 gene signature and display enhanced proliferation and fitness in response to an exogenous CXCL8 ligand in vitro. Genetic deletion of Cxcr2 in the hMPLW515L-adoptive transfer model abrogates fibrosis and extends overall survival, and pharmacologic inhibition of the CXCR1/2 pathway improves hematologic parameters, attenuates bone marrow fibrosis, and synergizes with JAK inhibitor therapy. Our mechanistic insights provide a rationale for therapeutic targeting of the CXCL8/CXCR2 pathway among patients with MF.

Cross-platform analysis reveals cellular and molecular landscape of glioblastoma invasion.

BACKGROUND: Improved treatment of glioblastoma (GBM) needs to address tumor invasion, a hallmark of the disease that remains poorly understood. In this study, we profiled GBM invasion through integrative analysis of histological and single-cell RNA sequencing (scRNA-seq) data from 10 patients. METHODS: Human histology samples, patient-derived xenograft mouse histology samples, and scRNA-seq data were collected from 10 GBM patients. Tumor invasion was characterized and quantified at the phenotypic level using hematoxylin and eosin and Ki-67 histology stains. Crystallin alpha B (CRYAB) and CD44 were identified as regulators of tumor invasion from scRNA-seq transcriptomic data and validated in vitro, in vivo, and in a mouse GBM resection model. RESULTS: At the cellular level, we found that invasive GBM are less dense and proliferative than their non-invasive counterparts. At the molecular level, we identified unique transcriptomic features that significantly contribute to GBM invasion. Specifically, we found that CRYAB significantly contributes to postoperative recurrence and is highly co-expressed with CD44 in invasive GBM samples. CONCLUSIONS: Collectively, our analysis identifies differentially expressed features between invasive and nodular GBM, and describes a novel relationship between CRYAB and CD44 that contributes to tumor invasiveness, establishing a cellular and molecular landscape of GBM invasion.

Polyaniline/Graphene-Functionalized Flexible Waste Mask Sensors for Ammonia and Volatile Sulfur Compound Monitoring.

A flexible resistive-type polyaniline-based gas sensor was fabricated by simple dip-coating of graphene combined with in situ polymerization of aniline on a flexible waste mask substrate. The prepared polypropylene/graphene/polyaniline (PP/G/PANI) hybrid sensor demonstrated a fast response (114 s) and recovery time (23 s), ppb-level detection limit (100 ppb), high response value (250% toward 50 ppm NH3, which is over four times greater than that of the pristine PANI sensor), acceptable flexibility, excellent selectivity, and long-term stability at room temperature. The morphological and structural properties of the composite sensor materials were characterized by scanning electron microscopy and energy-dispersive spectroscopy characterization, and the surface chemistry of the hybrid sensors was analyzed by Fourier transform infrared spectroscopy. The excellent sensing performance was mainly ascribed to the larger specific surface area and efficient conducting paths of the porous PP/G/PANI network. Moreover, the PP/G/PANI hybrid gas sensor exhibited excellent sensing capability on volatile sulfur compounds contained in human breath, indicating that the hybrid sensor can be applied to breath analysis and kidney disease diagnosis.

Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface.

Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54±7.78% (288 trials), 85.76 ±8.00% (240 trials), 84.65±8.11% (192 trials), and 83.29 ±8.25% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.

Microfluidic immuno-serology assay revealed a limited diversity of protection against COVID-19 in patients with altered immunity.

The immune response to SARS-CoV-2 for patients with altered immunity such as hematologic malignancies and autoimmune disease may differ substantially from that in general population. These patients remain at high risk despite wide-spread adoption of vaccination. It is critical to examine the differences at the systems level between the general population and the patients with altered immunity in terms of immunologic and serological responses to COVID-19 infection and vaccination. Here, we developed a novel microfluidic chip for high-plex immuno-serological assay to simultaneously measure up to 50 plasma or serum samples for up to 50 soluble markers including 35 plasma proteins, 11 anti-spike/RBD IgG antibodies spanning all major variants, and controls. Our assay demonstrated the quintuplicate test in a single run with high throughput, low sample volume input, high reproducibility and high accuracy. It was applied to the measurement of 1,012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein association matrix analysis revealed distinct immune mediator protein modules that exhibited a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies and patients with autoimmune disorders receiving B cell depletion therapy. Serological analysis identified that COVID infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which could be associated with limited clonotype diversity and functional deficiency in B cells and was further confirmed by single-cell BCR and transcriptome sequencing. These findings underscore the importance to individualize immunization strategy for these high-risk patients and provide an informative tool to monitor their responses at the systems level.