A quadri-fluorescence SARS-CoV-2 pseudovirus system for efficient antigenic characterization of multiple circulating variants.

The ongoing co-circulation of multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains necessitates advanced methods such as high-throughput multiplex pseudovirus systems for evaluating immune responses to different variants, crucial for developing updated vaccines and neutralizing antibodies (nAbs). We have developed a quadri-fluorescence (qFluo) pseudovirus platform by four fluorescent reporters with different spectra, allowing simultaneous measurement of the nAbs against four variants in a single test. qFluo shows high concordance with the classical single-reporter assay when testing monoclonal antibodies and human plasma. Utilizing qFluo, we assessed the immunogenicities of the spike of BA.5, BQ.1.1, XBB.1.5, and CH.1.1 in hamsters. An analysis of cross-neutralization against 51 variants demonstrated superior protective immunity from XBB.1.5, especially against prevalent strains such as "FLip" and JN.1, compared to BA.5. Our finding partially fills the knowledge gap concerning the immunogenic efficacy of the XBB.1.5 vaccine against current dominant variants, being instrumental in vaccine-strain decisions and insight into the evolutionary path of SARS-CoV-2.

In-line imaging and recognition of flip chip fabrication defects by real-time photoacoustic remote sensing system.

Microscopic defects in flip chips, originating from manufacturing, significantly affect performance and longevity. Post-fabrication sampling methods ensure product functionality but lack in-line defect monitoring to enhance chip yield and lifespan in real-time. This study introduces a photoacoustic remote sensing (PARS) system for in-line imaging and defect recognition during flip-chip fabrication. We first propose a real-time PARS imaging method based on continuous acquisition combined with parallel processing image reconstruction to achieve real-time imaging during the scanning of flip-chip samples, reducing reconstruction time from an average of approximately 1134 ms to 38 ms. Subsequently, we propose improved YOLOv7 with space-to-depth block (IYOLOv7-SPD), an enhanced deep learning defect recognition method, for accurate in-line recognition and localization of microscopic defects during the PARS real-time imaging process. The experimental results validate the viability of the proposed system for enhancing the lifespan and yield of flip-chip products in chip manufacturing facilities.

Antiviral Nanobiologic Therapy Remodulates Innate Immune Responses to Highly Pathogenic Coronavirus.

Highly pathogenic coronavirus (CoV) infection induces a defective innate antiviral immune response coupled with the dysregulated release of proinflammatory cytokines and finally results in acute respiratory distress syndrome (ARDS). A timely and appropriate triggering of innate antiviral response is crucial to inhibit viral replication and prevent ARDS. However, current medical countermeasures can rarely meet this urgent demand. Here, an antiviral nanobiologic named CoVR-MV is developed, which is polymerized of CoVs receptors based on a biomimetic membrane vesicle system. The designed CoVR-MV interferes with the viral infection by absorbing the viruses with maximized viral spike target interface, and mediates the clearance of the virus through its inherent interaction with macrophages. Furthermore, CoVR-MV coupled with the virus promotes a swift production and signaling of endogenous type I interferon via deregulating 7-dehydrocholesterol reductase (DHCR7) inhibition of interferon regulatory factor 3 (IRF3) activation in macrophages. These sequential processes re-modulate the innate immune responses to the virus, trigger spontaneous innate antiviral defenses, and rescue infected Syrian hamsters from ARDS caused by SARS-CoV-2 and all tested variants.

Development and validation of a simple tool composed of items on dyspnea, respiration rates, and C-reactive protein for pneumonia prediction among acute febrile respiratory illness patients in primary care settings.

BACKGROUND: Acute febrile respiratory illness (AFRI) patients are susceptible to pneumonia and suffer from significant morbidity and mortality throughout the world. In primary care settings, the situation is worse. Limited by computerized tomography resources and physician experiences, AFRI patients in primary care settings may not be diagnosed appropriately, which would affect following treatment. In this study, we aimed to develop and validate a simple prediction model to help physicians quickly identify AFRI patients of pneumonia risk in primary care settings. METHODS: A total of 1977 AFRI patients were enrolled at two fever clinics in Shanghai, China, and among them, 727 patients who underwent CT scans were included in the analysis. Acute alveolar or interstitial infiltrates found on CT images were diagnosed with pneumonia. Characteristics and blood parameters were compared between pneumonia and non-pneumonia patients. Then a multivariable model for pneumonia prediction was developed through logistic regression analysis. Its value for pneumonia prediction was prospectively assessed in an external multi-center population, which included 1299 AFRI patients in primary settings from 5 different provinces throughout China. RESULTS: In the model development population, pneumonia patients (n = 227) had a longer duration of fever; higher frequencies of purulent sputum, dyspnea, and thoracic pain; and higher levels of respiration rates and C-reactive protein (CRP) than non-pneumonia patients (n = 500). Logistic regression analysis worked out a model composed of items on dyspnea, respiration rates > 20/min, and CRP > 20 mg/l (DRC) for pneumonia prediction with an area under curve (AUC) of 0.8506. In the external validation population, the predictive accuracy of the DRC model was the highest when choosing at least one positive item (1 score) as a cut-off point with a sensitivity of 87.0% and specificity of 80.5%. DRC scores increased with pneumonia severity and lung lobe involvement and showed good performance for both bacterial and viral pneumonia. For viral pneumonia, dyspnea plus respiration rates > 20/min had good predictive capacity regardless of CRP concentration. CONCLUSIONS: DRC model is a simple tool that predicts pneumonia among AFRI patients, which would help physicians utilize medical resources rationally in primary care settings.

Cross-species tropism and antigenic landscapes of circulating SARS-CoV-2 variants.

Mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD) may alter viral host tropism and affect the activities of neutralizing antibodies. Here, we investigated 153 RBD mutants and 11 globally circulating variants of concern (VOCs) and variants of interest (VOIs) (including Omicron) for their antigenic changes and cross-species tropism in cells expressing 18 ACE2 orthologs. Several RBD mutations strengthened viral infectivity in cells expressing ACE2 orthologs of non-human animals, particularly those less susceptible to the ancestral strain. The mutations surrounding amino acids (aas) 439-448 and aa 484 are more likely to cause neutralization resistance. Strikingly, enhanced cross-species infection potential in the mouse and ferret, instead of the neutralization-escape scores of the mutations, account for the positive correlation with the cumulative prevalence of mutations in humans. These findings present insights for potential drivers of circulating SARS-CoV-2 variants and provide informative parameters for tracking and forecasting spreading mutations.

Drought stress strengthens the link between chlorophyll fluorescence parameters and photosynthetic traits.

Chlorophyll fluorescence (ChlF) has been used to understand photosynthesis and its response to climate change, particularly with satellite-based data. However, it remains unclear how the ChlF ratio and photosynthesis are linked at the leaf level under drought stress. Here, we examined the link between ChlF ratio and photosynthesis at the leaf level by measuring photosynthetic traits, such as net CO2 assimilation rate (An), the maximum carboxylation rate of Rubisco (Vcmax), the maximum rate of electron transport (Jmax), stomatal conductance (gs) and total chlorophyll content (Chlt). The ChlF ratio of the leaf level such as maximum quantum efficiency of PSII (Fv/Fm) is based on fluorescence kinetics. ChlF intensity ratio (LD685/LD740) based on spectrum analysis was obtained. We found that a combination of the stomatal limitation, non-stomatal limitation, and Chlt regulated leaf photosynthesis under drought stress, while Jmax and Chlt governed the ChlF ratio. A significant link between the ChlF ratio and An was found under drought stress while no significant correlation in the control, which indicated that drought stress strengthens the link between the ChlF ratio and photosynthetic traits. These results suggest that the ChlF ratio can be a powerful tool to track photosynthetic traits of terrestrial ecosystems under drought stress.

Optofluidic microcapillary biosensor for label-free, low glucose concentration detection.

We demonstrate label-free detection of low glucose concentration based on whispery gallery mode resonance in an optofluidic microcapillary (OFMC) biosensor. The wall surface of the OFMC is bio-chemically functionalized to detect cellular-level glucose concentration as low as 2.78 mM. The measured sensitivity is 0.966 pm/mM. The fabricated microcapillary has a thin wall thickness of 2 µm and a Q factor of 1.3 × 106, corresponding to a bulk refractive index sensitivity of 23.36 nm/RIU. The OFMC biosensor has advantages such as high resistance to environmental perturbation, small sample volume down to 90 nL and cost-effectiveness. Theoretical analysis shows the sensor sensitivity depends on the microcapillary wall thickness and liquid core refractive index.