Single-particle rotational sensing for analyzing the neutralization activity of antiviral antibodies.

Due to the pathogen-specific targeting, neutralization capabilities, and enduring efficacy, neutralizing antibodies (NAs) have received widespread attentions as a critical immunotherapeutic strategy against infectious viruses. However, because of the high variability and complexity of pathogens, rapid determination of neutralization activity of antiviral antibodies remains a challenge. Here, we report a new method, named as out-of-plane polarization imaging based single-particle rotational sensing, for rapid analysis of neutralization activity of antiviral antibody against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the spike protein functionalized gold nanorods (AuNRs) and angiotensin-converting enzyme 2 (ACE2) coated gold nanoparticles (AuNPs) as the rotational sensors and chaperone probes, we demonstrated the single-particle rotational sensing strategy for the measurement of rotational diffusion coefficient of the chaperone-bound rotational sensors caused by the specific spike protein-ACE2 interactions. This enables us to measure the neutralizing activity of neutralizing antibody from the analysis of dose-dependent changes in rotational diffusion coefficient (Dr) of the rotational sensors upon the treatment of SARS-CoV-2 antibody. With this technique, we achieved the quantitative determination of neutralization activity of a commercially available SARS-CoV-2 antibody (IC50, 294.1 ng/mL) with satisfying accuracy and anti-interference ability. This simple and robust method holds the potential for rapid and accurate evaluation of neutralization activity against different pathogenic viruses.

WNT2B high­expressed fibroblasts induce the fibrosis of IBD by promoting NK cells secreting IL-33.

Fibrosis is an important pathological change in inflammatory bowel disease (IBD), but the mechanism has yet to be elucidated. WNT2B high­expressed fibroblasts are enriched in IBD intestinal tissues, although the precise function of this group of fibroblasts remains unclear. This study investigated whether WNT2B high­expressed fibroblasts aggravated intestinal tissue damage and fibrosis. Our study provides evidence that WNT2B high­expressed fibroblasts and NK cells were enriched in colitis tissue of patients with IBD. WNT2B high­expressed fibroblasts secreted wnt2b, which bound to FZD4 on NK cells and activated the NF-κB and STAT3 pathways to enhance IL-33 expression. TCF4, a downstream component of the WNT/ß-catenin pathway, bound to p65 and promoted binding to IL-33 promoter. Furthermore, Salinomycin, an inhibitor of the WNT/ß-catenin pathway, inhibited IL-33 secretion in colitis, thereby reducing intestinal inflammation.Knocking down WNT2B reduces NK cell infiltration and IL-33 secretion in colitis, and reduce intestinal inflammation and fibrosis. In conclusion, WNT2B high­expressed fibroblasts activate NK cells by secreting wnt2b, which activates the WNT/ß-catenin and NF-κB pathways to promote IL-33 expression and secretion, potentially culminating in the induction of colonic fibrosis in IBD. KEY MESSAGES: WNT2B high-expressed fibroblasts and NK cells are enriched in colitis tissue, promoting NK cells secreting IL-33. Wnt2b activates NF-κB and STAT3 pathways promotes IL-33 expression by activating p65 and not STAT3. syndrome TCF4 binds to p65 and upregulates the NF- κB pathway. Salinomycin reduces NK cell infiltration and IL-33 secretion in colitis. Knocking down WNT2B mitigates inflammation and fibrosis in chronic colitis.

Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.

Background: The incidence rate of thyroid nodules has reached 65%, but only 5-15% of these modules are malignant. Therefore, accurately determining the benign and malignant nature of thyroid nodules can prevent unnecessary treatment. We aimed to develop a deep-learning (DL) radiomics model based on ultrasound (US), explore its diagnostic efficacy for benign and malignant thyroid nodules, and verify whether it improved the diagnostic level of physicians. Methods: We retrospectively included 1,076 thyroid nodules from 817 patients at three institutions. The radiomics and DL features of the US images were extracted and used to construct radiomics signature (Rad_sig) and deep-learning signature (DL_sig). A Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. Clinical US semantic signature (C_US_sig) was constructed based on clinical information and US semantic features. Next, a combined model was constructed based on the above three signatures in the form of a nomogram. The model was constructed using a development set (institution 1: 719 nodules), and the model was evaluated using two external validation sets (institution 2: 74 nodules, and institution 3: 283 nodules). The performance of the model was assessed using decision curve analysis (DCA) and calibration curves. Furthermore, the C_US_sigs of junior physicians, senior physicians, and expers were constructed. The DL radiomics model was used to assist the physicians with different levels of experience in the interpretation of thyroid nodules. Results: In the development and validation sets, the combined model showed the highest performance, with areas under the curve (AUCs) of 0.947, 0.917, and 0.929, respectively. The DCA results showed that the comprehensive nomogram had the best clinical utility. The calibration curves indicated good calibration for all models. The AUCs for distinguishing between benign and malignant thyroid nodules by junior physicians, senior physicians, and experts were 0.714-0.752, 0.740-0.824, and 0.891-0.908, respectively; however, with the assistance of DL radiomics, the AUCs reached 0.858-0.923, 0.888-0.944, and 0.912-0.919, respectively. Conclusions: The nomogram based on DL radiomics had high diagnostic efficacy for thyroid nodules, and DL radiomics could assist physicians with different levels of experience to improve their diagnostic level.

Genome-Wide Characterization and Expression Profiling of the AP2/ERF Gene Family in Fragaria vesca L.

The wild strawberry (Fragaria vesca L.; F. vesca) represents a resilient and extensively studied model organism. While the AP2/ERF gene family plays a pivotal role in plant development, its exploration within F. vesca remains limited. In this study, we characterized the AP2/ERF gene family in wild strawberries using the recently released genomic data (F. vesca V6.0). We conducted an analysis of the gene family expansion pattern, we examined gene expression in stem segments and leaves under cold conditions, and we explored its functional attributes. Our investigation revealed that the FvAP2/ERF family comprises 86 genes distributed among four subfamilies: AP2 (17), RAV (6), ERF (62), and Soloist (1). Tandem and segmental duplications significantly contributed to the growth of this gene family. Furthermore, predictive analysis identified several cis-acting elements in the promoter region associated with meristematic tissue expression, hormone regulation, and resistance modulation. Transcriptomic analysis under cold stress unveiled diverse responses among multiple FvAP2/ERFs in stem segments and leaves. Real-time fluorescence quantitative reverse transcription PCR (RT-qPCR) results confirmed elevated expression levels of select genes following the cold treatment. Additionally, overexpression of FvERF23 in Arabidopsis enhanced cold tolerance, resulting in significantly increased fresh weight and root length compared to the wild-type control. These findings lay the foundation for further exploration into the functional roles of FvAP2/ERF genes.

Trends in resource allocation for primary health care in rural China: Concentration curve and decomposition analysis.

Since 2009, China has made large investments in strengthening the primary healthcare system. This study aimed to examine the trends in the number and distribution of health resources in rural China following the health system reform and to decompose the sources of inequalities. Data were collected from standardized reports compiled by each county in rural China and compiled by the National Health Commission and Bureau of Statistics. From the findings of this empirical study, resource allocation per capita for primary health care (PHC) improved gradually from 2008 to 2014. The distribution of beds across counties (ranked by level of economic development) was relatively equitable. However, the concentration curve analysis indicated that the distribution of primary care professionals remained skewed in favour of wealthier and more urbanised counties. Economic status was proved to be a major contributor to the inequality of health human resource. China's primary care reforms resulted in simultaneously improved supply of PHC resources as well as pro-rich inequality in distribution of the workforce. To advance equality in health resource allocation, greater attention should be paid to the substantial inequality of economic status within counties.

Influence of eye-related behavior on myopia among junior middle school students under the background of double reduction during the COVID-19 pandemic.

BACKGROUND: To investigate the changes in the unhealthy eye-related behaviors of junior middle school students during the COVID-19 pandemic and the double reduction policy and its relationship with myopia. METHODS: Data were obtained from the 2019-2022 Tianjin Children and Youth Myopia, Common Diseases and Health Influencing Factors Survey. Latent profile analysis (LPA) and a generalized linear model (GLM) were applied to analyze the effect of eye-related behavior classes on myopia. RESULTS: A total of 2508 junior middle school students were included. The types of eye-related behavior were categorized into the medium-healthy behavior group, heavy academic burden and near-eye behavior group, insufficient lighting group and high-healthy behavior group. Students with heavy academic burdens and near-eye behavior were more likely to develop myopia than were those in the high-healthy group (OR = 1.466, 95% CI = 1.203-1.787; P < 0.001). CONCLUSIONS: The dual reduction policy has a positive effect on improving unhealthy eye-related behaviors, and the prevention and control of myopia through the use of different combinations of eye-related behaviors are heterogeneous among junior middle school students. In the post-COVID-19 period, we should continue to implement a double reduction policy and formulate targeted eye-related behavior strategies to provide an important reference for the prevention and control of myopia among children and adolescents during public health emergencies in the future.

Plasmonic Imaging of Multivalent NTD-Nucleic Acid Interactions for Broad-Spectrum Antiviral Drug Analysis.

The discovery and identification of broad-spectrum antiviral drugs are of great significance for blocking the spread of pathogenic viruses and corresponding variants of concern. Herein, we proposed a plasmonic imaging-based strategy for assessing the efficacy of potential broad-spectrum antiviral drugs targeting the N-terminal domain of a nucleocapsid protein (NTD) and nucleic acid (NA) interactions. With NTD and NA conjugated gold nanoparticles as core and satellite nanoprobes, respectively, we found that the multivalent binding interactions could drive the formation of core-satellite nanostructures with enhanced scattering brightness due to the plasmonic coupling effect. The core-satellite assembly can be suppressed in the presence of antiviral drugs targeting the NTD-NA interactions, allowing the drug efficacy analysis by detecting the dose-dependent changes in the scattering brightness by plasmonic imaging. By quantifying the changes in the scattering brightness of plasmonic nanoprobes, we uncovered that the constructed multivalent weak interactions displayed a 500-fold enhancement in affinity as compared with the monovalent NTD-NA interactions. We demonstrated the plasmonic imaging-based strategy for evaluating the efficacy of a potential broad-spectrum drug, PJ34, that can target the NTD-NA interactions, with the IC50 as 24.35 and 14.64 µM for SARS-CoV-2 and SARS-CoV, respectively. Moreover, we discovered that ceftazidime holds the potential as a candidate drug to inhibit the NTD-NA interactions with an IC50 of 22.08 µM from molecular docking and plasmonic imaging-based drug analysis. Finally, we validated that the potential antiviral drug, 5-benzyloxygramine, which can induce the abnormal dimerization of nucleocapsid proteins, is effective for SARS-CoV-2, but not effective against SARS-CoV. All these demonstrations indicated that the plasmonic imaging-based strategy is robust and can be used as a powerful strategy for the discovery and identification of broad-spectrum drugs targeting the evolutionarily conserved viral proteins.

Reusable magnetic molecular imprinted polymers based on magnetic graphene oxide for selective identification and detection of eugenol in environmental water samples.

In this study, a reliable method for determining eugenol content in environmental water samples was established by combining magnetic solid-phase extraction with high-performance liquid chromatography. Magnetic molecular imprinted polymers MGO@MIPs were prepared through surface molecular imprinting technique with eugenol as the template molecule. The material displayed good superparamagnetic properties and magnetic responsiveness in favor of rapid separation. The adsorption properties of MGO@MIPs for eugenol were evaluated through adsorption kinetics and selectivity experiments. MGO@MIPs were found to have favorable reusability and obvious selectivity for eugenol. In addition, adsorption and elution conditions were investigated. Under optimal conditions, a linear relationship was obtained between the concentration of eugenol and its peak area in the range of 0.02-5 mg/L (R2 = 0.9998) and the limit of detection was 4.0 × 10-6 mg/mL. The performance of the established method was assessed with the average recovery of 96.59-102.20% and the relative standard deviation (RSD) below 3.5%. The application of this method provides a new perspective for the separation, enrichment and detection of eugenol in water environment.

A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China.

Long-term, daily, and gap-free Normalized Difference Vegetation Index (NDVI) is of great significance for a better Earth system observation. However, gaps and contamination are quite severe in current daily NDVI datasets. This study developed a daily 0.05° gap-free NDVI dataset from 1981-2023 in China by combining valid data identification and spatiotemporal sequence gap-filling techniques based on the National Oceanic and Atmospheric Administration daily NDVI dataset. The generated NDVI in more than 99.91% of the study area showed an absolute percent bias (|PB|) smaller than 1% compared with the original valid data, with an overall R2 and root mean square error (RMSE) of 0.79 and 0.05, respectively. PB and RMSE between our dataset and the MODIS daily gap-filled NDVI dataset (MCD19A3CMG) during 2000 to 2023 are 7.54% and 0.1, respectively. PB between our dataset and three monthly NDVI datasets (i.e., GIMMS3g, MODIS MOD13C2, and SPOT/PROBA) are only -5.79%, 4.82%, and 2.66%, respectively. To the best of our knowledge, this is the first long-term daily gap-free NDVI in China by far.

Cognitive Function, Healthy Lifestyle, and All-Cause Mortality among Chinese Older Adults: A Longitudinal Prospective Study.

BACKGROUND: Both cognitive decline and unhealthy lifestyles have been linked to an elevated risk of mortality in older people. We aimed to investigate whether a healthy lifestyle might modify the association between cognitive function and all-cause mortality in Chinese older populations. METHODS: The final analysis included 5124 individuals free of dementia, selected from the Chinese Longitudinal Healthy Longevity Survey from 2011 to 2018. Cognitive function was assessed in 2011 using the Mini-Mental State Examination (MMSE). A lifestyle score was calculated based on five lifestyle factors, including smoking, alcohol consumption, physical activity, diet, and body mass index. Cox proportional hazards models were performed to evaluate the association between baseline cognitive function and the risk of all-cause mortality, with an interaction term of cognitive function and lifestyle score being added to the models. RESULTS: The average age of participants was 81.87 years old at baseline. During a median follow-up of 6.4 years, 1461 deaths were documented. Both higher cognitive function (HR: 0.96; 95% CI: 0.96-0.97) and a healthier lifestyle (HR: 0.92; 95% CI: 0.87-0.97) were significantly associated with a reduced risk of mortality. We found that lifestyle significantly modified the association of cognitive function with mortality (p for interaction = 0.004). The inverse relation between cognitive function and mortality was found to be more pronounced among participants with a healthier lifestyle. Of note, among the lifestyle scores component, diet showed a significant interaction with mortality (p for interaction = 0.003), and the protective HR of the all-cause mortality associated with higher MMSE scores was more prominent among participants with healthy diets compared with unhealthy diets. CONCLUSIONS: Our study indicates that cognitive decline is associated with a higher risk of mortality, and such associations are attenuated by maintaining a healthy lifestyle, with a particular emphasis on healthy diet.

An overview of PROTACs targeting MDM2 as a novel approach for cancer therapy.

MDM2 genes amplification or altered expression is commonly observed in various cancers bearing wild-type TP53. Directly targeting the p53-binding pocket of MDM2 to activate the p53 pathway represents a promising therapeutic approach. Despite the development of numerous potent MDM2 inhibitors that have advanced into clinical trials, their utility is frequently hampered by drug resistance and hematologic toxicity such as neutropenia and thrombocytopenia. The emergence of PROTAC technology has revolutionized drug discovery and development, with applications in both preclinical and clinical research. Harnessing the power of PROTAC molecules to achieve MDM2 targeted degradation and p53 reactivation holds significant promise for cancer therapy. In this review, we summarize representative MDM2 PROTAC degraders and provide insights for researchers investigating MDM2 proteins and the p53 pathway.

Overexpression of a Fragaria vesca NAM, ATAF, and CUC (NAC) Transcription Factor Gene (FvNAC29) Increases Salt and Cold Tolerance in Arabidopsis thaliana.

The NAC (NAM, ATAF1/2, CUC2) family of transcription factors (TFs) is a vital transcription factor family of plants. It controls multiple parts of plant development, tissue formation, and abiotic stress response. We cloned the FvNAC29 gene from Fragaria vesca (a diploid strawberry) for this research. There is a conserved NAM structural domain in the FvNAC29 protein. The highest homology between FvNAC29 and PaNAC1 was found by phylogenetic tree analysis. Subcellular localization revealed that FvNAC29 is localized onto the nucleus. Compared to other tissues, the expression level of FvNAC29 was higher in young leaves and roots. In addition, Arabidopsis plants overexpressing FvNAC29 had higher cold and high-salinity tolerance than the wild type (WT) and unloaded line with empty vector (UL). The proline and chlorophyll contents of transgenic Arabidopsis plants, along with the activities of the antioxidant enzymes like catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) under 200 mM NaCl treatment or -8 °C treatment, were higher than those activities of the control. Meanwhile, malondialdehyde (MDA) and the reactive oxygen species (ROS) content were higher in the WT and UL lines. FvNAC29 improves transgenic plant resistance to cold and salt stress by regulating the expression levels of AtRD29a, AtCCA1, AtP5CS1, and AtSnRK2.4. It also improves the potential to tolerate cold stress by positively regulating the expression levels of AtCBF1, AtCBF4, AtCOR15a, and AtCOR47. These findings suggest that FvNAC29 may be related to the processes and the molecular mechanisms of F. vesca response to high-salinity stress and LT stress, providing a comprehensive understanding of the NAC TFs.

The role of glycosylation in non-productive adsorption of cellulase to lignin isolated from pretreated corn stover.

Glycosylation, a general post-translational modification for fungal cellulase, has been shown to affect cellulase binding to its substrate. However, the exact impact of glycosylation on cellulase-lignin interaction remain unclear. Here, we demonstrated that the lignin isolated from tetrahydrofuran-pretreated corn stover exhibits strong adsorption capability to cellulase due to its negatively charged and porous structure. For the cellulases with varying glycosylation levels, the less-glycosylated protein showed high adsorption capability to lignin, and that trend was observed for the main cellulase components secreted by Penicillium oxilicum, including endoglucanase PoCel5B, cellobiohydrolase PoCel7A-2, and ß-glucosidase PoBgl1. Additionally, N-glycan sites and motifs were examined using mass spectrometry, and protein structures with N-glycans were constructed, where PoBgl1 and PoCel7A-2 contained 13 and 1 glycosylated sites respectively. The results of molecular dynamics simulations indicated that the N-glycans impacted on the solvent-accessible surface area and secondary structure of protein, and the binding conformation of lignin fragment on cellulase, resulting in a decrease in binding energy (14 kcal/mol for PoBgl1 and 13 kcal/mol for PoCel7A-2), particularly for van der Waals and electrostatic interaction. Those findings suggested that glycosylation negatively impacted the lignin-cellulase interaction, providing a theoretical basis for the rational engineering of enzymes to reduce lignin-enzyme interaction.

A One-Stone-Two-Birds Strategy of Targeting Microbubbles with "Dual" Anti-Inflammatory and Blood-Brain Barrier "Switch" Function for Ischemic Stroke Treatment.

Inflammation is considered to be the main target of the development of new stroke therapies. There are three key issues in the treatment of stroke inflammation: the first one is how to overcome the blood-brain barrier (BBB) to achieve drug delivery, the second one is how to select drugs to treat stroke inflammation, and the third one is how to achieve targeted drug delivery. In this study, we constructed hydrocortisone-phosphatidylserine microbubbles and combined them with ultrasound (US)-targeted microbubble destruction technology to successfully open the BBB to achieve targeted drug delivery. Phosphatidylserine on the microbubbles was used for its "eat me" effect to increase the targeting of the microvesicles. In addition, we found that hydrocortisone can accelerate the closure of the BBB, achieving efficient drug delivery while reducing the entry of peripheral toxins into the brain. In the treatment of stroke inflammation, it was found that hydrocortisone itself has anti-inflammatory effects and can also change the polarization of microglia from the harmful pro-inflammatory M1 phenotype to the beneficial anti-inflammatory M2 phenotype, thus achieving dual anti-inflammatory effects and enhancing the anti-inflammatory effects in ischemic areas after stroke, well reducing the cerebellar infarction volume by inhibiting the inflammatory response after cerebral ischemia. A confocal microendoscope was used to directly observe the polarization of microglial cells in living animal models for dynamic microscopic visualization detection showing the advantage of being closer to clinical work. Taken together, this study constructed a multifunctional targeted US contrast agent with the function of "one-stone-two-birds", which can not only "on-off" the BBB but also have "two" anti-inflammatory functions, providing a new strategy of integrated anti-inflammatory targeted delivery and imaging monitoring for ischemic stroke treatment.

Lignin-xylan nanospheres prepared by green and quick method from lignocellulose and used as additive in PVA films.

Lignin, as an amorphous three-dimensional aromatic polymer, was able to self-assemble into lignin nanoparticles (LNPs) to realize valorization of lignin. Here, lignin-xylan extractives were extracted from grape seed (GS) and poplar by acidic THF at room temperature, and effectively produced lignin-xylan nanospheres via spin evaporation. The morphology and chemical properties of nanospheres were determined by its natural origins, consequently influencing its application. For the lignin-xylan extractive from grape seed, the lignin was composed of guaiacyl (G) and p-hydroxylphenyl (H) units and the hollowed nanospheres (GS-LNPs) with 362.72 nm diameter was produced. The extractive from poplar was composed of G-syringyl (S) typed lignin (80.30 %) and xylan (12.33 %), that can assemble into LNPs with smaller size (229.87 nm), better PDI (0.1), and light color. The hybrid particles showed the qualities of lignin and xylan, that properties led to the LNPs@PVA composite films with UV-blocking capability, strong mechanical strength and hydrophobicity, and transparency ability of visible light. P-LNPs showed better performance as the film additives, due to its lower particles size and high content of unconjugated -OH from xylan. Xylan was significant in the composite films, and lowering the xylan content resulted in the decrease of the composite film's mechanical properties and hydrophobicity.

Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study.

Background: Acute kidney injury (AKI) is a common and serious organ dysfunction in critically ill children. Early identification and prediction of AKI are of great significance. However, current AKI criteria are insufficiently sensitive and specific, and AKI heterogeneity limits the clinical value of AKI biomarkers. This study aimed to establish and validate an explainable prediction model based on the machine learning (ML) approach for AKI, and assess its prognostic implications in children admitted to the pediatric intensive care unit (PICU). Methods: This multicenter prospective study in China was conducted on critically ill children for the derivation and validation of the prediction model. The derivation cohort, consisting of 957 children admitted to four independent PICUs from September 2020 to January 2021, was separated for training and internal validation, and an external data set of 866 children admitted from February 2021 to February 2022 was employed for external validation. AKI was defined based on serum creatinine and urine output using the Kidney Disease: Improving Global Outcome (KDIGO) criteria. With 33 medical characteristics easily obtained or evaluated during the first 24 h after PICU admission, 11 ML algorithms were used to construct prediction models. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley Additive exPlanation method was used to rank the feature importance and explain the final model. A probability threshold for the final model was identified for AKI prediction and subgrouping. Clinical outcomes were evaluated in various subgroups determined by a combination of the final model and KDIGO criteria. Findings: The random forest (RF) model performed best in discriminative ability among the 11 ML models. After reducing features according to feature importance rank, an explainable final RF model was established with 8 features. The final model could accurately predict AKI in both internal (AUC = 0.929) and external (AUC = 0.910) validations, and has been translated into a convenient tool to facilitate its utility in clinical settings. Critically ill children with a probability exceeding or equal to the threshold in the final model had a higher risk of death and multiple organ dysfunctions, regardless of whether they met the KDIGO criteria for AKI. Interpretation: Our explainable ML model was not only successfully developed to accurately predict AKI but was also highly relevant to adverse outcomes in individual children at an early stage of PICU admission, and it mitigated the concern of the "black-box" issue with an undirect interpretation of the ML technique. Funding: The National Natural Science Foundation of China, Jiangsu Province Science and Technology Support Program, Key talent of women's and children's health of Jiangsu Province, and Postgraduate Research & Practice Innovation Program of Jiangsu Province.

The transcription factor ELF4 alleviates inflammatory bowel disease by activating IL1RN transcription, suppressing inflammatory TH17 cell activity, and inducing macrophage M2 polarization.

Background: Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder affecting millions worldwide. Due to the complexity of its pathogenesis, the treatment options for IBD are limited. This study focuses on ELF4, a member of the ETS transcription factor family, as a target to elucidate its role in IBD and investigate its mechanism of action in alleviating IBD symptoms by activating IL1RN transcription to suppress the activity of inflammatory TH17 cells. Methods: Using the GEO database, this study examined LPS-induced intestinal inflammatory genes and their regulation mechanisms. We examined the colon length of LPS-treated mice and derived the Disease Activity Index (DAI). H&E staining, ELISA, and flow cytometry were used to detect mice colon tissue damage, inflammatory factor levels in mouse serum, mouse macrophage types and inflammatory TH17 cell activity. RT-qPCR and Western blot detected ELF4, IL1RN, M1, and M2 polarization markers. In Vitro, using dual-luciferase and ChIP assays, we tested mouse bone marrow-derived macrophages (BMDMs) and mouse intestinal epithelial cells for IL1RN promoter activity and ELF4 enrichment. Results: Bioinformatics showed that LPS-induced colitis animals have reduced ELF4 expression in their colon tissue. In vivo tests confirmed reduced ELF4 expression in mice with LPS-induced colitis. ELF4 overexpression reduced mouse intestinal inflammation. ELF4 activated IL1RN transcription in bioinformatics and in vitro tests. ELF4 promoted IL1RN transcription and macrophage M2 polarization to limit intestinal epithelial cell death and inflammation and reduce mouse intestinal inflammation in vitro. ELF4 also reduced the Th17/Treg ratio by increasing IL1RN transcription. Conclusion: ELF4 activates IL1RN transcription, suppresses inflammatory TH17 cells, and induces macrophage M2 polarization to treat IBD.

Bioinformatics approach to identify the hub gene associated with COVID-19 and idiopathic pulmonary fibrosis.

The coronavirus disease 2019 (COVID-19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS-CoV-2 infection, deserves attention. As COVID-19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID-19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID-19. A risk prediction model was developed to assess the prognosis of patients infected with SARS-CoV-2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS-CoV-2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID-19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID-19. With the increasing availability of COVID-19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.

Large Stokes shift fluorescent RNAs for dual-emission fluorescence and bioluminescence imaging in live cells.

Fluorescent RNAs, aptamers that bind and activate small fluorogenic dyes, have provided a particularly attractive approach to visualizing RNAs in live cells. However, the simultaneous imaging of multiple RNAs remains challenging due to a lack of bright and stable fluorescent RNAs with bio-orthogonality and suitable spectral properties. Here, we develop the Clivias, a series of small, monomeric and stable orange-to-red fluorescent RNAs with large Stokes shifts of up to 108 nm, enabling the simple and robust imaging of RNA with minimal perturbation of the target RNA's localization and functionality. In combination with Pepper fluorescent RNAs, the Clivias enable the single-excitation two-emission dual-color imaging of cellular RNAs and genomic loci. Clivias can also be used to detect RNA-protein interactions by bioluminescent imaging both in live cells and in vivo. We believe that these large Stokes shift fluorescent RNAs will be useful tools for the tracking and quantification of multiple RNAs in diverse biological processes.

High-density Au nanoshells assembled onto Fe3O4 nanoclusters for integrated enrichment and photothermal/colorimetric dual-mode detection of SARS-CoV-2 nucleocapsid protein.

Traditional lateral flow immunoassays (LFIA) suffer from insufficient sensitivity, difficulty for quantitation, and susceptibility to complex substrates, limiting their practical application. Herein, we developed a polyethylenimine (PEI)-mediated approach for assembling high-density Au nanoshells onto Fe3O4 nanoclusters (MagAushell) as LFIA labels for integrated enrichment and photothermal/colorimetric dual-mode detection of SARS-CoV-2 nucleocapsid protein (N protein). PEI layer served not only as "binders" to Fe3O4 nanoclusters and Au nanoshells, but also "barriers" to ambient environment. Thus, MagAushell not only combined magnetic and photothermal properties, but also showed good stability. With MagAushell, N protein was first separated and enriched from complex samples, and then loaded to the strip for detection. By observation of the color stripes, qualitative detection was performed with naked eye, and by measuring the temperature change under laser irradiation, quantification was attained free of sophisticated instruments. The introduction of Fe3O4 nanoclusters facilitated target purification and enrichment before LFIA, which greatly improved the anti-interference ability and increased the detection sensitivity by 2 orders compared with those without enrichment. Moreover, the high loading density of Au nanoshells on one Fe3O4 nanocluster enhanced the photothermal signal of the nanoprobe significantly, which could further increase the detection sensitivity. The photothermal detection limit reached 43.64 pg/mL which was 1000 times lower than colloidal gold strips. Moreover, this method was successfully applied to real samples, showing great application potential in practice. We envision that this LFIA could serve not only for SARS-CoV-2 detection but also as a general test platform for other biotargets in clinical samples.