Distinct Functional Humoral Immune Responses Are Induced after Live Attenuated and Inactivated Seasonal Influenza Vaccination.

Influenza viruses infect 5-30% of the world's population annually, resulting in millions of incidents of hospitalization and thousands of mortalities worldwide every year. Although annual vaccination has significantly reduced hospitalization rates in vulnerable populations, the current vaccines are estimated to offer a wide range of protection from 10 to 60% annually. Such incomplete immunity may be related to both poor antigenic coverage of circulating strains, as well as to the insufficient induction of protective immunity. Beyond the role of hemagglutinin (HA) and neuraminidase (NA), vaccine-induced Abs have the capacity to induce a broader array of Ab effector functions, including Ab-dependent cellular cytotoxicity, that has been implicated in universal immunity against influenza viruses. However, whether different vaccine platforms can induce functional humoral immunity in a distinct manner remains incompletely defined. In this study, we compared vaccine-induced humoral immune responses induced by two seasonal influenza vaccines in Homo sapiens, the i.m. inactivated vaccine (IIV/Fluzone) and the live attenuated mucosal vaccine (LAIV/FluMist). Whereas the inactivated influenza vaccine induced superior Ab titers and FcγR binding capacity to diverse HA and NA Ags, the live attenuated influenza mucosal vaccine induced a more robust functional humoral immune response against both the HA and NA domains. Multivariate Ab analysis further highlighted the significantly different overall functional humoral immune profiles induced by the two vaccines, marked by differences in IgG titers, FcR binding, and both NK cell-recruiting and opsonophagocytic Ab functions. These results highlight the striking differences in Ab Fc-effector profiles induced systemically by two distinct influenza vaccine platforms.

Neurologic sequelae of COVID-19 are determined by immunologic imprinting from previous coronaviruses.

Coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global public health emergency. Although SARS-CoV-2 is primarily a respiratory pathogen, extra-respiratory organs, including the CNS, can also be affected. Neurologic symptoms have been observed not only during acute SARS-CoV-2 infection, but also at distance from respiratory disease, also known as long-COVID or neurological post-acute sequelae of COVID-19 (neuroPASC). The pathogenesis of neuroPASC is not well understood, but hypotheses include SARS-CoV-2-induced immune dysfunctions, hormonal dysregulations and persistence of SARS-CoV-2 reservoirs. In this prospective cohort study, we used a high throughput systems serology approach to dissect the humoral response to SARS-CoV-2 (and other common coronaviruses: 229E, HKU1, NL63 and OC43) in the serum and CSF from 112 infected individuals who developed (n = 18) or did not develop (n = 94) neuroPASC. Unique SARS-CoV-2 humoral profiles were observed in the CSF of neuroPASC compared with serum responses. All antibody isotypes (IgG, IgM, IgA) and subclasses (IgA1-2, IgG1-4) were detected in serum, whereas CSF was characterized by focused IgG1 (and absence of IgM). These data argue in favour of compartmentalized brain-specific responses against SARS-CoV-2 through selective transfer of antibodies from the serum to the CSF across the blood-brain barrier, rather than intrathecal synthesis, where more diversity in antibody classes/subclasses would be expected. Compared to individuals who did not develop post-acute complications following infection, individuals with neuroPASC had similar demographic features (median age 65 versus 66.5 years, respectively, P = 0.55; females 33% versus 44%, P = 0.52) but exhibited attenuated systemic antibody responses against SARS-CoV-2, characterized by decreased capacity to activate antibody-dependent complement deposition (ADCD), NK cell activation (ADNKA) and to bind Fcγ receptors. However, surprisingly, neuroPASC individuals showed significantly expanded antibody responses to other common coronaviruses, including 229E, HKU1, NL63 and OC43. This biased humoral activation across coronaviruses was particularly enriched in neuroPASC individuals with poor outcome, suggesting an 'original antigenic sin' (or immunologic imprinting), where pre-existing immune responses against related viruses shape the response to the current infection, as a key prognostic marker of neuroPASC disease. Overall, these findings point to a pathogenic role for compromised anti-SARS-CoV-2 responses in the CSF, likely resulting in incomplete virus clearance from the brain and persistent neuroinflammation, in the development of post-acute neurologic complications of SARS-CoV-2 infection.

Computational investigation of blood cell transport in retinal microaneurysms.

Microaneurysms (MAs) are one of the earliest clinically visible signs of diabetic retinopathy (DR). MA leakage or rupture may precipitate local pathology in the surrounding neural retina that impacts visual function. Thrombosis in MAs may affect their turnover time, an indicator associated with visual and anatomic outcomes in the diabetic eyes. In this work, we perform computational modeling of blood flow in microchannels containing various MAs to investigate the pathologies of MAs in DR. The particle-based model employed in this study can explicitly represent red blood cells (RBCs) and platelets as well as their interaction in the blood flow, a process that is very difficult to observe in vivo. Our simulations illustrate that while the main blood flow from the parent vessels can perfuse the entire lumen of MAs with small body-to-neck ratio (BNR), it can only perfuse part of the lumen in MAs with large BNR, particularly at a low hematocrit level, leading to possible hypoxic conditions inside MAs. We also quantify the impacts of the size of MAs, blood flow velocity, hematocrit and RBC stiffness and adhesion on the likelihood of platelets entering MAs as well as their residence time inside, two factors that are thought to be associated with thrombus formation in MAs. Our results show that enlarged MA size, increased blood velocity and hematocrit in the parent vessel of MAs as well as the RBC-RBC adhesion promote the migration of platelets into MAs and also prolong their residence time, thereby increasing the propensity of thrombosis within MAs. Overall, our work suggests that computational simulations using particle-based models can help to understand the microvascular pathology pertaining to MAs in DR and provide insights to stimulate and steer new experimental and computational studies in this area.

Multiphysics and multiscale modeling of microthrombosis in COVID-19.

Emerging clinical evidence suggests that thrombosis in the microvasculature of patients with Coronavirus disease 2019 (COVID-19) plays an essential role in dictating the disease progression. Because of the infectious nature of SARS-CoV-2, patients' fresh blood samples are limited to access for in vitro experimental investigations. Herein, we employ a novel multiscale and multiphysics computational framework to perform predictive modeling of the pathological thrombus formation in the microvasculature using data from patients with COVID-19. This framework seamlessly integrates the key components in the process of blood clotting, including hemodynamics, transport of coagulation factors and coagulation kinetics, blood cell mechanics and adhesive dynamics, and thus allows us to quantify the contributions of many prothrombotic factors reported in the literature, such as stasis, the derangement in blood coagulation factor levels and activities, inflammatory responses of endothelial cells and leukocytes to the microthrombus formation in COVID-19. Our simulation results show that among the coagulation factors considered, antithrombin and factor V play more prominent roles in promoting thrombosis. Our simulations also suggest that recruitment of WBCs to the endothelial cells exacerbates thrombogenesis and contributes to the blockage of the blood flow. Additionally, we show that the recent identification of flowing blood cell clusters could be a result of detachment of WBCs from thrombogenic sites, which may serve as a nidus for new clot formation. These findings point to potential targets that should be further evaluated, and prioritized in the anti-thrombotic treatment of patients with COVID-19. Altogether, our computational framework provides a powerful tool for quantitative understanding of the mechanism of pathological thrombus formation and offers insights into new therapeutic approaches for treating COVID-19 associated thrombosis.

In silico biophysics and hemorheology of blood hyperviscosity syndrome.

Hyperviscosity syndrome (HVS) is characterized by an increase of the blood viscosity by up to seven times the normal blood viscosity, resulting in disturbances to the circulation in the vasculature system. HVS is commonly associated with an increase of large plasma proteins and abnormalities in the properties of red blood cells, such as cell interactions, cell stiffness, and increased hematocrit. Here, we perform a systematic study of the effect of each biophysical factor on the viscosity of blood by employing the dissipative particle dynamic method. Our in silico platform enables manipulation of each parameter in isolation, providing a unique scheme to quantify and accurately investigate the role of each factor in increasing the blood viscosity. To study the effect of these four factors independently, each factor was elevated more than its values for a healthy blood while the other factors remained constant, and viscosity measurement was performed for different hematocrits and flow rates. Although all four factors were found to increase the overall blood viscosity, these increases were highly dependent on the hematocrit and the flow rates imposed. The effect of cell aggregation and cell concentration on blood viscosity were predominantly observed at low shear rates, in contrast to the more magnified role of cell rigidity and plasma viscosity at high shear rates. Additionally, cell-related factors increase the whole blood viscosity at high hematocrits compared with the relative role of plasma-related factors at lower hematocrits. Our results, mapped onto the flow rates and hematocrits along the circulatory system, provide a correlation to underpinning mechanisms for HVS findings in different blood vessels.

Quantifying Fibrinogen-Dependent Aggregation of Red Blood Cells in Type 2 Diabetes Mellitus.

Fibrinogen is regarded as the main glycoprotein in the aggregation of red blood cells (RBCs), a normally occurring phenomenon that has a major impact on blood rheology and hemodynamics, especially under pathological conditions, including type 2 diabetes mellitus (T2DM). In this study, we investigate the fibrinogen-dependent aggregation dynamics of T2DM RBCs through patient-specific predictive computational simulations that invoke key parameters derived from microfluidic experiments. We first calibrate our model parameters at the doublet (a rouleau consisting of two aggregated RBCs) level for healthy blood samples by matching the detaching force required to fully separate RBC doublets with measurements using atomic force microscopy and optical tweezers. Using results from companion microfluidic experiments that also provide in vitro quantitative information on cell-cell adhesive dynamics, we then quantify the rouleau dissociation dynamics at the doublet and multiplet (a rouleau consisting of three or more aggregated RBCs) levels for obese patients with or without T2DM. Specifically, we examine the rouleau breakup rate when it passes through microgates at doublet level and investigate the effect of rouleau alignment in altering its breakup pattern at multiplet level. This study seamlessly integrates in vitro experiments and simulations and consequently enhances our understanding of the complex cell-cell interaction, highlighting the importance of the aggregation and disaggregation dynamics of RBCs in patients at increased risk of microvascular complications.

Quantifying Shear-Induced Deformation and Detachment of Individual Adherent Sickle Red Blood Cells.

Vaso-occlusive crisis, a common painful complication of sickle cell disease, is a complex process triggered by intercellular adhesive interactions among blood cells and the endothelium in all human organs (e.g., the oxygen-rich lung as well as hypoxic systems such as liver and kidneys). We present a combined experimental-computational study to quantify the adhesive characteristics of sickle mature erythrocytes (SMEs) and irreversibly sickled cells (ISCs) under flow conditions mimicking those in postcapillary venules. We employed an in vitro microfluidic cell adherence assay, which is coated uniformly with fibronectin. We investigated the adhesion dynamics of SMEs and ISCs in pulsatile flow under well-controlled hypoxic conditions, inferring the cell adhesion strength by increasing the flow rate (or wall shear stress (WSS)) until the onset of cell detachment. In parallel, we performed simulations of individual SMEs and ISCs under shear. We introduced two metrics to quantify the adhesion process, the cell aspect ratio (AR) as a function of WSS and its rate of change (the dynamic deformability index). We found that the AR of SMEs decreases significantly with the increase of WSS, consistent between the experiments and simulations. In contrast, the AR of ISCs remains constant in time and independent of the flow rate. The critical WSS value for detaching a single SME in oxygenated state is in the range of 3.9-5.5 Pa depending on the number of adhesion sites; the critical WSS value for ISCs is lower than that of SMEs. Our simulations show that the critical WSS value for SMEs in deoxygenated state is above 6.2 Pa (multiple adhesion sites), which is greater than their oxygenated counterparts. We investigated the effect of cell shear modulus on the detachment process; we found that for the same cell adhesion spring constant, the higher shear modulus leads to an earlier cell detachment from the functionalized surface. These findings may aid in the understanding of individual roles of sickle cell types in sickle cell disease vaso-occlusion.

Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.

Patients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, especially among the elderly, demanding urgent medical attention. Accurate prediction of SH remains challenging due to its multifaced nature, contributed from factors such as medications, lifestyle choices, and metabolic measurements. In this study, we propose a systematic approach to improve the robustness and accuracy of SH predictions using machine learning models, guided by clinical feature selection. Our focus is on developing long-term SH prediction models using both semi-supervised learning and supervised learning algorithms. Using the action to control cardiovascular risk in diabetes trial, which includes electronic health records for over 10,000 individuals, we focus on studying adults with T2DM. Our results indicate that the application of a multi-view co-training method, incorporating the random forest algorithm, improves the specificity of SH prediction, while the same setup with Naive Bayes replacing random forest demonstrates better sensitivity. Our framework also provides interpretability of machine learning models by identifying key predictors for hypoglycemia, including fasting plasma glucose, hemoglobin A1c, general diabetes education, and NPH or L insulins. The integration of data routinely available in electronic health records significantly enhances our model's capability to predict SH events, showcasing its potential to transform clinical practice by facilitating early interventions and optimizing patient management. By enhancing prediction accuracy and identifying crucial predictive features, our study contributes to advancing the understanding and management of hypoglycemia in this population.

Repeated vaccination with homologous influenza hemagglutinin broadens human antibody responses to unmatched flu viruses.

The on-going diversification of influenza virus necessicates annual vaccine updating. The vaccine antigen, the viral spike protein hemagglutinin (HA), tends to elicit strain-specific neutralizing activity, predicting that sequential immunization with the same HA strain will boost antibodies with narrow coverage. However, repeated vaccination with homologous SARS-CoV-2 vaccine eventually elicits neutralizing activity against highly unmatched variants, questioning this immunological premise. We evaluated a longitudinal influenza vaccine cohort, where each year the subjects received the same, novel H1N1 2009 pandemic vaccine strain. Repeated vaccination gradually enhanced receptor-blocking antibodies (HAI) to highly unmatched H1N1 strains within individuals with no initial memory recall against these historical viruses. An in silico model of affinity maturation in germinal centers integrated with a model of differentiation and expansion of memory cells provides insight into the mechanisms underlying these results and shows how repeated exposure to the same immunogen can broaden the antibody response against diversified targets.

Protocol for near-infrared optogenetics manipulation of neurons and motor behavior in C. elegans using emissive upconversion nanoparticles.

In deep tissue, optogenetics faces limitations with visible light. Here, we present a protocol for near-infrared (NIR) optogenetics manipulation of neurons and motor behavior in Caenorhabditis elegans using emissive upconversion nanoparticles (UCNPs). We describe steps for synthesizing and modifying UCNPs. We then detail procedures for regulating neurons using these UCNPs in the model organism C. elegans. Using NIR light allows for superior tissue penetration to manipulate neuronal activities and locomotion behavior. For complete details on the use and execution of this protocol, please refer to Guo et al.,1 Ao et al.,2 and Zhang et al.3.

Lupus-associated innate receptors drive extrafollicular evolution of autoreactive B cells.

In systemic lupus erythematosus, recent findings highlight the extrafollicular (EF) pathway as prominent origin of autoantibody-secreting cells (ASCs). CD21loCD11c+ B cells, associated with aging, infection, and autoimmunity, are contributors to autoreactive EF ASCs but have an obscure developmental trajectory. To study EF kinetics of autoreactive B cell in tissue, we adoptively transferred WT and gene knockout B cell populations into the 564Igi mice - an autoreactive host enriched with autoantigens and T cell help. Time-stamped analyses revealed TLR7 dependence in early escape of peripheral B cell tolerance and establishment of a pre-ASC division program. We propose CD21lo cells as precursors to EF ASCs due to their elevated TLR7 sensitivity and proliferative nature. Blocking receptor function reversed CD21 loss and reduced effector cell generation, portraying CD21 as a differentiation initiator and a possible target for autoreactive B cell suppression. Repertoire analysis further delineated proto-autoreactive B cell selection and receptor evolution toward self-reactivity. This work elucidates receptor and clonal dynamics in EF development of autoreactive B cells, and establishes modular, native systems to probe mechanisms of autoreactivity.

Humoral profiles of toddlers and young children following SARS-CoV-2 mRNA vaccination.

Although young children generally experience mild symptoms following infection with SARS-CoV-2, severe acute and long-term complications can occur. SARS-CoV-2 mRNA vaccines elicit robust immunoglobulin profiles in children ages 5 years and older, and in adults, corresponding with substantial protection against hospitalizations and severe disease. Whether similar immune responses and humoral protection can be observed in vaccinated infants and young children, who have a developing and vulnerable immune system, remains poorly understood. To study the impact of mRNA vaccination on the humoral immunity of infant, we use a system serology approach to comprehensively profile antibody responses in a cohort of children ages 6 months to 5 years who were vaccinated with the mRNA-1273 COVID-19 vaccine (25 µg). Responses are compared with vaccinated adults (100 µg), in addition to naturally infected toddlers and young children. Despite their lower vaccine dose, vaccinated toddlers elicit a functional antibody response as strong as adults, with higher antibody-dependent phagocytosis compared to adults, without report of side effects. Moreover, mRNA vaccination is associated with a higher IgG3-dependent humoral profile against SARS-CoV-2 compared to natural infection, supporting that mRNA vaccination is effective at eliciting a robust antibody response in toddlers and young children.

Hybrid immunity expands the functional humoral footprint of both mRNA and vector-based SARS-CoV-2 vaccines.

Despite the successes of current coronavirus disease 2019 (COVID-19) vaccines, waning immunity, the emergence of variants of concern, and breakthrough infections among vaccinees have begun to highlight opportunities to improve vaccine platforms. Real-world vaccine efficacy studies have highlighted the reduced risk of breakthrough infections and diseases among individuals infected and vaccinated, referred to as hybrid immunity. Thus, we sought to define whether hybrid immunity shapes the humoral immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) following Pfizer/BNT162b2, Moderna mRNA-1273, ChadOx1/AZD1222, and Ad26.COV2.S vaccination. Each vaccine exhibits a unique functional humoral profile in vaccination only or hybrid immunity. However, hybrid immunity shows a unique augmentation of S2-domain-specific functional immunity that was poorly induced for the vaccination only. These data highlight the importance of natural infection in breaking the immunodominance away from the evolutionarily unstable S1 domain and potentially affording enhanced cross-variant protection by targeting the more highly conserved S2 domain of SARS-CoV-2.

Liver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive steatotic liver disease indexes: Results from a large multi-center study.

BACKGROUND: Non-invasive tools (NIT) for metabolic-dysfunction associated liver disease (MASLD) screening or diagnosis need to be thoroughly validated using liver biopsies. PURPOSE: To externally validate NITs designed to differentiate the presence or absence of liver steatosis as well as more advanced disease stages, to confirm fully validated indexes (n = 7 NITs), to fully validate partially validated indexes (n = 5 NITs), and to validate for the first time one new index (n = 1 NIT). METHODS: This is a multi-center study from two Gastroenterology-Hepatology Departments (Greece and Australia) and one Bariatric-Metabolic Surgery Department (Italy). Overall, n = 455 serum samples of patients with biopsy-proven MASLD (n = 374, including 237 patients with metabolic-dysfunction associated steatohepatitis (MASH)) and Controls (n = 81) were recruited. A complete validation analysis was performed to differentiate the presence of MASLD vs. Controls, MASH vs. metabolic-dysfunction associated steatotic liver (MASL), histological features of MASH, and fibrosis stages. RESULTS: The index of NASH (ION) demonstrated the highest differentiation ability for the presence of MASLD vs. Controls, with the area under the curve (AUC) being 0.894. For specific histological characterization of MASH, no NIT demonstrated adequate performance, while in the case of specific features of MASH, such as hepatocellular ballooning and lobular inflammation, ION demonstrated the best performance with AUC being close to or above 0.850. For fibrosis (F) classification, the highest AUC was reached by the aspartate aminotransferase to platelet ratio index (APRI) being ~0.850 yet only with the potential to differentiate the severe fibrosis stages (F3, F4) vs. mild or moderate fibrosis (F0-2) with an AUC > 0.900 in patients without T2DM. When we excluded patients with morbid obesity, the differentiation ability of APRI was improved, reaching AUC = 0.802 for differentiating the presence of fibrosis F2-4 vs. F0-1. The recommended by current guidelines index FIB-4 seemed to differentiate adequately between severe (i.e., F3-4) and mild or moderate fibrosis (F0-2) with an AUC = 0.820, yet this was not the case when FIB-4 was used to classify patients with fibrosis F2-4 vs. F0-1. Trying to improve the predictive value of all NITs, using Youden's methodology, to optimize the suggested cut-off points did not materially improve the results. CONCLUSIONS: The validation of currently available NITs using biopsy-proven samples provides new evidence for their ability to differentiate between specific disease stages, histological features, and, most importantly, fibrosis grading. The overall performance of the examined NITs needs to be further improved for applications in the clinic.

Beta-spike-containing boosters induce robust and functional antibody responses to SARS-CoV-2 in macaques primed with distinct vaccines.

The reduced effectiveness of COVID-19 vaccines due to the emergence of variants of concern (VOCs) necessitated the use of vaccine boosters to bolster protection against disease. However, it remains unclear how boosting expands protective breadth when primary vaccine platforms are distinct and how boosters containing VOC spike(s) broaden humoral responses. Here, we report that boosters composed of recombinant spike antigens of ancestral (prototype) and Beta VOCs elicit a robust, pan-VOC, and multi-functional humoral response in non-human primates largely independent of the primary vaccine series platform. Interestingly, Beta-spike-containing boosters stimulate immunoglobulin A (IgA) with a greater breadth of recognition in protein-primed recipients when administered with adjuvant system 03 (AS03). Our results highlight the utility of a component-based booster strategy for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for broad humoral recognition, independent of primary vaccine series. This is of high global health importance given the heterogeneity of primary vaccination platforms distributed.

Humoral profiles of toddlers and young children following SARS-CoV-2 mRNA vaccination.

Although young children generally experience mild symptoms following infection with SARS-CoV-2, severe acute and long-term complications can occur. SARS-CoV-2 mRNA vaccines elicit robust immunoglobulin profiles in children ages 5 years and older, and in adults, corresponding with substantial protection against hospitalizations and severe disease. Whether similar immune responses and humoral protection can be observed in vaccinated infants and young children, who have a developing and vulnerable immune system, remains poorly understood. To study the impact of mRNA vaccination on the humoral immunity of infant, we used a system serology approach to comprehensively profile antibody responses in a cohort of children ages 6 months to 5 years who were vaccinated with the mRNA-1273 COVID-19 vaccine (25 µg). Responses were compared with vaccinated adults (100 µg), in addition to naturally infected toddlers and young children. Despite their lower vaccine dose, vaccinated toddlers elicited a stronger functional antibody response than adults, including against variant of concerns (VOCs), without report of side effects. Moreover, mRNA vaccination was associated with a higher IgG3-dependent humoral profile against SARS-CoV-2 compared to natural infection, supporting that mRNA vaccination is effective at eliciting a robust antibody response in toddlers and young children.

Beta-containing bivalent SARS-CoV-2 protein vaccine elicits durable broad neutralization in macaques and protection in hamsters.

BACKGROUND: Since the beginning of the COVID-19 pandemic, several variants of concern (VOC) have emerged for which there is evidence of an increase in transmissibility, more severe disease, and/or reduced vaccine effectiveness. Effective COVID-19 vaccine strategies are required to achieve broad protective immunity against current and future VOC. METHODS: We conducted immunogenicity and challenge studies in macaques and hamsters using a bivalent recombinant vaccine formulation containing the SARS-CoV-2 prefusion-stabilized Spike trimers of the ancestral D614 and the variant Beta strains with AS03 adjuvant (CoV2 preS dTM-AS03) in a primary immunization setting. RESULTS: We show that a primary immunization with the bivalent CoV2 preS dTM-AS03 elicits broader and durable (1 year) neutralizing antibody responses against VOC including Omicron BA.1 and BA.4/5, and SARS-CoV-1 as compared to the ancestral D614 or Beta variant monovalent vaccines in naïve non-human primates. In addition, the bivalent formulation confers protection against viral challenge with SARS-CoV-2 prototype D614G strain as well as Alpha and Beta variant strains in hamsters. CONCLUSIONS: Our findings demonstrate the potential of a Beta-containing bivalent CoV2 preS dTM-AS03 formulation to provide broad and durable immunogenicity, as well as protection against VOC in naïve populations.

SARS-CoV-2 has changed over time, resulting in different forms of the virus called variants. These variants compromise the protection offered by the COVID-19 vaccines, which trigger an immune response against the viral Spike protein that allows the virus to attach and infect human cells, since their spike proteins are different. Here, we developed and tested a vaccine containing two different Spike proteins, one from the original Wuhan strain and another from the Beta variant. In macaques, the vaccine leads to the production of antibodies able to stop all variants tested from infecting human cells, including Omicron, with stable levels over one year. In hamsters, the vaccine protected against infection with the ancestral virus and the Alpha and Beta variants. Our findings have important implications for vaccine control of existing and future SARS-CoV-2 variants of concern.

Recent Advances in Computational Modeling of Biomechanics and Biorheology of Red Blood Cells in Diabetes.

Diabetes mellitus, a metabolic disease characterized by chronically elevated blood glucose levels, affects about 29 million Americans and more than 422 million adults all over the world. Particularly, type 2 diabetes mellitus (T2DM) accounts for 90-95% of the cases of vascular disease and its prevalence is increasing due to the rising obesity rates in modern societies. Although multiple factors associated with diabetes, such as reduced red blood cell (RBC) deformability, enhanced RBC aggregation and adhesion to the endothelium, as well as elevated blood viscosity are thought to contribute to the hemodynamic impairment and vascular occlusion, clinical or experimental studies cannot directly quantify the contributions of these factors to the abnormal hematology in T2DM. Recently, computational modeling has been employed to dissect the impacts of the aberrant biomechanics of diabetic RBCs and their adverse effects on microcirculation. In this review, we summarize the recent advances in the developments and applications of computational models in investigating the abnormal properties of diabetic blood from the cellular level to the vascular level. We expect that this review will motivate and steer the development of new models in this area and shift the attention of the community from conventional laboratory studies to combined experimental and computational investigations, aiming to provide new inspirations for the development of advanced tools to improve our understanding of the pathogenesis and pathology of T2DM.

[Policy Analysis in Plastic Pollution Governance and Recommendations in China].

Plastic pollution is a global concern and an issue in environmental governance. Based on the updated "Plastic Prohibition/Restriction Order" issued recently in China, the present study systematically reviewed the implementation effectiveness of the "Plastic Prohibition/Restriction Order" since 2007. Furthermore, we summarized plastics in China and plastic waste management progress policies. Additionally, three deficiencies of the updated "Plastic Prohibition/Restriction Order" were discussed:policy formulation, implementation, and supervision. Some positive recommendations were provided based on the available reports, such as integrating plastic pollution into national basic laws and regulation systems, building a network platform for public attendance, and coordinating the publicity of the "Plastic prohibition/Restriction Order" with the public interest. Besides these measurements, some points about plastic waste management in the future were also highlighted, such as the "blind area" in small retail stores, the rational sharing of environmental protection responsibility, new materials and processes, and recycling and disposal systems for plastic wastes. Most importantly, the present study could provide ideas for policy-makers to address plastic pollution at its sources.

AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images.

Purpose: Accurate segmentation of microaneurysms (MAs) from adaptive optics scanning laser ophthalmoscopy (AOSLO) images is crucial for identifying MA morphologies and assessing the hemodynamics inside the MAs. Herein, we introduce AOSLO-net to perform automatic MA segmentation from AOSLO images of diabetic retinas. Method: AOSLO-net is composed of a deep neural network based on UNet with a pretrained EfficientNet as the encoder. We have designed customized preprocessing and postprocessing policies for AOSLO images, including generation of multichannel images, de-noising, contrast enhancement, ensemble and union of model predictions, to optimize the MA segmentation. AOSLO-net is trained and tested using 87 MAs imaged from 28 eyes of 20 subjects with varying severity of diabetic retinopathy (DR), which is the largest available AOSLO dataset for MA detection. To avoid the overfitting in the model training process, we augment the training data by flipping, rotating, scaling the original image to increase the diversity of data available for model training. Results: The validity of the model is demonstrated by the good agreement between the predictions of AOSLO-net and the MA masks generated by ophthalmologists and skillful trainees on 87 patient-specific MA images. Our results show that AOSLO-net outperforms the state-of-the-art segmentation model (nnUNet) both in accuracy (e.g., intersection over union and Dice scores), as well as computational cost. Conclusions: We demonstrate that AOSLO-net provides high-quality of MA segmentation from AOSLO images that enables correct MA morphological classification. Translational Relevance: As the first attempt to automatically segment retinal MAs from AOSLO images, AOSLO-net could facilitate the pathological study of DR and help ophthalmologists make disease prognoses.