Synergistic promotion of angiogenesis after intracerebral hemorrhage by ginsenoside Rh2 and chrysophanol in rats.

BACKGROUND: Intracerebral hemorrhage (ICH) is a debilitating condition characterized by the rupture of cerebral blood vessels, resulting in profound neurological deficits. A significant challenge in the treatment of ICH lies in the brain's limited capacity to regenerate damaged blood vessels. This study explores the potential synergistic effects of Ginsenoside Rh2 and Chrysophanol in promoting angiogenesis following ICH in a rat model. METHODS: Network pharmacology was employed to predict the potential targets and pathways of Ginsenoside Rh2 and Chrysophanol for ICH treatment. Molecular docking was utilized to assess the binding affinity between these compounds and their respective targets. Experimental ICH was induced in male Sprague-Dawley rats through stereotactic injection of type VII collagenase into the right caudate putamen (CPu). The study encompassed various methodologies, including administration protocols, assessments of neurological function, magnetic resonance imaging, histological examination, observation of brain tissue ultrastructure, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), immunofluorescence staining, Western blot analysis, and statistical analyses. RESULTS: Network pharmacology analysis indicated that Ginsenoside Rh2 and Chrysophanol may exert their therapeutic effects in ICH by promoting angiogenesis. Results from animal experiments revealed that rats treated with Ginsenoside Rh2 and Chrysophanol exhibited significantly improved neurological function, reduced hematoma volume, and diminished pathological injury compared to the Model group. Immunofluorescence analysis demonstrated enhanced expression of vascular endothelial growth factor receptor 2 (VEGFR2) and CD31, signifying augmented angiogenesis in the peri-hematomal region following combination therapy. Importantly, the addition of a VEGFR2 inhibitor reversed the increased expression of VEGFR2 and CD31. Furthermore, Western blot analysis revealed upregulated expression of angiogenesis-related factors, including VEGFR2, SRC, AKT1, MAPK1, and MAPK14, in the combination therapy group, but this effect was abrogated upon VEGFR2 inhibitor administration. CONCLUSION: The synergistic effect of Ginsenoside Rh2 and Chrysophanol demonstrated a notable protective impact on ICH injury in rats, specifically attributed to their facilitation of angiogenesis. Consequently, this research offers a foundation for the utilization of Ginsenosides Rh2 and Chrysophanol in medical settings and offers direction for the advancement of novel pharmaceuticals for the clinical management of ICH.

Acupuncture for post-stroke depression: a systematic review and network meta-analysis.

BACKGROUND: Patients with post-stroke depression (PSD) usually experience anxiety, hopelessness, and insomnia, which have a negative impact on their daily activities and post-stroke rehabilitation. Acupuncture (AC), as a minimally invasive technique, has become a popular choice for improving depression symptoms. However, it is still unclear which therapy is associated with the best outcomes for PSD. In this review, we aimed to explore the impact of AC in alleviating symptoms of PSD and to evaluate the difference in effectiveness between AC combined with pharmacotherapies and various non-pharmacotherapies. METHODS: Six databases and three clinical trials registration platforms were searched from inception to March 2023. Randomized clinical trial comparing needle-based AC with pharmacotherapy, and other non-pharmacotherapy or invalid group were included. Two independent reviewers identified eligible studies, and collected data using a pre-made form. A Bayesian network meta-analysis was conducted to assess and compare different techniques using RStudio 3.6.0 with the package 'GEMTC' V.0.8.1. The primary outcome was the efficacy for PSD assessed by scales measuring depressive symptoms. The secondary outcomes were effectiveness for neurological function and the quality of life. The ranking probabilities for all treatment interventions was performed using the Surface Under the Cumulative Ranking curve (SUCRA). The risk of bias was assessed by using the Revised Cochrane Risk of Bias tool 2. RESULTS: Sixty-two studies, involving 5308 participants published from 2003 to 2022, were included. The results showed that compared with western medicine (WM) (defined as pharmacotherapy for PSD), AC alone or with repetitive transcranial magnetic stimulation (RTMS), Traditional Chinese medicine (TCM) alone or with WM, were superior for alleviating depression symptoms. Compared to Usual Care, AC alone or plus other therapies could significantly decrease scores on the Hamilton Depression Rating scale. According to result of SUCRA, AC plus RTMS had the highest probability of improving depressive symptoms with a probability of 49.43%. CONCLUSIONS: The results of this study indicate that AC alone or combined with other therapies appears to be effective in improving depression symptoms of stroke survivors. Moreover, in comparison to WM, AC alone or plus RTMS, TCM, TCM with WM, or WM, were more effective in improving depression symptoms of PSD. Also, AC with RTMS seems to be the most effective with the highest probability. REGISTRATION: This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database in November 2020 and updated in July 2021. The registration number is CRD42020218752.

RNAi-Based Gene Silencing of RXLR Effectors Protects Plants Against the Oomycete Pathogen Phytophthora capsici.

Phytophthora capsici is a broad-host range oomycete pathogen that can cause severe phytophthora blight disease of pepper and hundreds of other plant species worldwide. Natural resistance against P. capsici is inadequate, and it is very difficult to control by most of existing chemical fungicides. Therefore, it is urgent to develop alternative strategies to control this pathogen. Recently, host-induced or spray-induced gene silencing of essential or virulent pathogen genes provided an effective strategy for disease controls. Here, we demonstrate that P. capsici can effectively take up small interfering RNAs (siRNAs) from the environment. According to RNA-seq and quantitative reverse transcription PCR analysis, we identified four P. capsici RXLR effector genes that are significantly up-regulated during the infection stage. Transient overexpression and promote-infection assays indicated that RXLR1 and RXLR4 could promote pathogen infection. Using a virus-induced gene silencing system in pepper plants, we found that in planta-expressing RNA interference (RNAi) constructs that target RXLR1 or RXLR4 could significantly reduce pathogen infection, while co-interfering RXLR1 and RXLR4 could confer a more enhanced resistance to P. capsici. We also found that exogenously applying siRNAs that target RXLR1 or RXLR4 could restrict growth of P. capsici on the pepper and Nicotiana benthamiana leaves; when targeting RXLR1 and RXLR4 simultaneously, the control effect was more remarkable. These data suggested that RNAi-based gene silencing of RXLR effectors has great potential for application in crop improvement against P. capsici and also provides an important basis for the development of RNA-based antioomycete agents.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Satellite-Enabled Internet of Remote Things Network Transmits Field Data from the Most Remote Areas of the Tibetan Plateau.

In this article, we employed a satellite-enabled Internet of Remote Things (IoRT) network as a promising solution to retrieve data in the most remote areas of interest, where public networks are absent. This article presents a system network based on the satellite-enabled IoRT, a new paradigm that defines a network where each environmental monitoring device can autonomously establish a network with a remote data center. The Xingyun satellite constellation was employed for data retrieval on the Tibetan Plateau (TP). The monitoring system was mainly composed of a ground Internet of Things (IoT) terminal that was built with satellite transceivers, environmental monitoring devices, and system software. We deployed five of these newly developed terminals in harsh areas to monitor environmental variables, and accordingly, air temperature and relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation were retrieved with the satellite-enabled IoRT network. Field experiments were conducted to evaluate the performance of the proposed system network, and the results indicated that the average time delay with and without the packet creation mode reached 32 and 32.7 s, respectively, and the average packet loss rate with and without the packet creation mode reached 5.63% and 4.48%, respectively. The successful implementation of the satellite-enabled IoRT network for the rapid retrieval of monitoring data in remote glacier, forestland, and canyon areas at very high altitudes on the TP provides an entirely new and revolutionary data retrieval means for backhauling data from remote areas of interest.

River Basin Cyberinfrastructure in the Big Data Era: An Integrated Observational Data Control System in the Heihe River Basin.

River basin cyberinfrastructure with the Internet of Things (IoT) as the core has brought watershed data science into the big data era, greatly improving data acquisition and sharing efficiency. However, challenges in analyzing, processing, and applying very large quantities of observational data remain. Given the observational needs in watershed research, we studied the construction of river basin cyberinfrastructure and developed an integrated observational data control system (IODCS). The IODCS is an important platform for processing large quantities of observational data, including automated collection, storage, analysis, processing, and release. This paper presents various aspects of the IODCS in detail, including the system's overall design, function realization, big data analysis methods, and integrated models. We took the middle reaches of the Heihe River Basin (HRB) as the application research area to show the performance of the developed system. Since the system began operation, it has automatically received, analyzed, and stored more than 1.4 billion observational data records, with an average of more than 14 million observational data records processed per month and up to 21,011 active users. The demonstrated results show that the IODCS can effectively leverage the processing capability of massive observational data and provide a new perspective for facilitating ecological and hydrological scientific research on the HRB.

Polymerization-Induced Coassembly of Enzyme-Polymer Conjugates into Comicelles with Tunable and Enhanced Cascade Activity.

Living organisms utilize spatially organized enzyme complexes to carry out signal transduction and metabolic pathways in an efficient and specific way. Herein, inspired by natural enzyme complexes, we report the polymerization-induced coassembly (PICA) of enzyme-polymer conjugates into comicelles with tunable and enhanced cascade activity by using the cascade reaction implemented by glucose oxidase (GOX) and horseradish peroxidase (HRP) as a model system. Notably, the cascade activity of GOX/HRP-polymer comicelles monotonically increases with the GOX/HRP ratio. The cascade activity of GOX/HRP-polymer comicelles is up to 4.9 times higher than that of free GOX and HRP mixtures at the same GOX/HRP ratio. We further demonstrate that our system can quickly detect glucose in contrast with a commercially available glucose assay kit. These findings provide a new and general method of PICA for the controlled construction of artificial enzyme complexes with tunable and enhanced activity in enzyme cascades for advanced biomedical applications.

The transcriptional reprograming and functional identification of WRKY family members in pepper's response to Phytophthora capsici infection.

BACKGROUND: Plant transcription factors (TFs) are key transcriptional regulators to manipulate the regulatory network of host immunity. However, the globally transcriptional reprogramming of plant TF families in response to pathogens, especially between the resistant and susceptible host plants, remains largely unknown. RESULTS: Here, we performed time-series RNA-seq from a resistant pepper line CM334 and a susceptible pepper line EC01 upon challenged with Phytophthora capsici, and enrichment analysis indicated that WRKY family most significantly enriched in both CM334 and EC01. Interestingly, we found that nearly half of the WRKY family members were significantly up-regulated, whereas none of them were down-regulated in the two lines. These induced WRKY genes were greatly overlapped between CM334 and EC01. More strikingly, most of these induced WRKY genes were expressed in time-order patterns, and could be mainly divided into three subgroups: early response (3 h-up), mid response (24 h-up) and mid-late response (ML-up) genes. Moreover, it was found that the responses of these ML-up genes were several hours delayed in EC01. Furthermore, a total of 19 induced WRKY genes were selected for functional identification by virus-induced gene silencing. The result revealed that silencing of CaWRKY03-6, CaWRKY03-7, CaWRKY06-5 or CaWRKY10-4 significantly increase the susceptibility to P. capsici both in CM334 and EC01, indicating that they might contribute to pepper's basal defense against P. capsici; while silencing of CaWRKY08-4 and CaWRKY01-10 significantly impaired the disease resistance in CM334 but not in EC01, suggesting that these two WRKY genes are prominent modulators specifically in the resistant pepper plants. CONCLUSIONS: These results considerably extend our understanding of WRKY gene family in pepper's resistance against P. capsici and provide potential applications for genetic improvement against phytophthora blight.

Data-Driven Anomaly Detection Approach for Time-Series Streaming Data.

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox.

Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time-frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

Polymerization Induced Self-Assembly of a Site-Specific Interferon α-Block Copolymer Conjugate into Micelles with Remarkably Enhanced Pharmacology.

Conjugating a hydrophilic and protein-resistant polymer to a protein is a widely used strategy to extend the in vivo half-life of the protein; however, the benefit of the half-life extension is usually limited by the bioactivity decrease. Herein we report a supramolecular self-assembly strategy of site-specific in situ polymerization induced self-assembly (SI-PISA) to address the dilemma. An amphiphilic block copolymer (POEGMA-PHPMA) was directly grown from the C-terminus of an important therapeutic protein interferon-α (IFN) to in situ form IFN-POEGMA-PHPMA conjugate micelles. Notably, the in vitro bioactivity of the micelles was 21.5-fold higher than that of the FDA-approved PEGylated interferon-α PEGASYS. Particularly, the in vivo half-life of the micelles (83.8 h) was 1.7- and 100-fold longer than those of PEGASYS (49.5 h) and IFN (0.8 h), respectively. In a tumor-bearing mouse model, the micelles completely suppressed tumor growth with 100% animal survival, whereas at the same dose, PEGASYS and IFN were much less effective. These findings suggest that SI-PISA is promising as a next-generation technology to remarkably enhance the pharmacological performance of therapeutic proteins with short circulation half-lives.

[Effect of Chinese Drugs for Breaking Blood Expelling Stasis on Acute Cerebral Hemorrhage: a Prospective Randomized Double-blind Controlled Study].

OBJECTIVE: To study the safety of using Chinese drugs for breaking blood expelling stasis (CDBBES) in hypertension patients with intracerebral hemorrhage within 6 h, and to observe whether they would result in hematoma enlargement. METHODS: A prospective randomized double-blind controlled clinical study was employed. Totally 128 cerebral hemorrhage patients within 6 h were recruited from 8 research centers from October 2013 to March 2015, and finally 76 of them were included. These patients were assigned to 3 groups by simple random sampling, group A, B, and C. Patients in group A (26 cases) took whole CDBBES recipe (containing leeches and equivalent insects). Those in group B (25 cases) took CDBBES recipe (removing leech and gradfly). Those in group C (25 cases) took placebos. Medication lasted for 10 successive days. The hematoma enlargement rate within 24 h, the occurrence of adverse reactions and adverse events were observed. To guarantee the safety of this trial, an interim analysis of first level unblinding was used. RESULTS: The hematoma enlargement rate was 11. 5% (3/26) in group A, 16. 0% (4/25) in group B, and 20. 0% (5/25) in group C. There was no statistical difference in the hematoma enlargement rate among the 3 groups (X² =0. 823, P =0. 682). Adverse reactions and adverse events occurred in 7 cases, 1 patient with acute myocardial infarction, 1 with chest op- pression and palpitation, 2 with diarrhea in group A. No patient had adverse reaction or adverse event in group B. And diarrhea occurred in 3 patients of group C. CONCLUSION: The interim analysis of first level unblinding showed that hematoma enlargement within 6 h was not resulted from using CDBBES.

Integrative Transcriptome Analysis of mRNA and miRNA in Pepper's Response to Phytophthora capsici Infection.

Phytophthora blight of pepper is a notorious disease caused by the oomycete pathogen Phytophthora capsici, which poses a great threat to global pepper production. MicroRNA (miRNA) is a class of non-coding small RNAs that regulate gene expressions by altering the translation efficiency or stability of targeted mRNAs, which play important roles in the regulation of a plant's response to pathogens. Herein, time-series mRNA-seq libraries and small RNA-seq libraries were constructed using pepper roots from the resistant line CM334 and the susceptible line EC01 inoculated with P. capsici at 0, 6, 24, and 48 h post-inoculation, respectively. For mRNA-seq analysis, a total of 2159 and 2971 differentially expressed genes (DEGs) were identified in CM334 and EC01, respectively. For miRNA-seq analysis, 491 pepper miRNAs were identified, including 330 known miRNAs and 161 novel miRNAs. Among them, 69 and 88 differentially expressed miRNAs (DEMs) were identified in CM334 and EC01, respectively. Examination of DEMs and their targets revealed 22 regulatory networks, predominantly featuring up-regulated miRNAs corresponding to down-regulated target genes. Notably, these DEM-DEG regulatory networks exhibited significant overlap between CM334 and EC01, suggesting that they might contribute to pepper's basal defense against P. capsici. Furthermore, five selected DEMs (miR166, miR1171, miR395, miR530 and miRN2) and their target genes underwent qRT-PCR validation, confirming a consistent negative correlation in the expression patterns of miRNAs and their targets. This comprehensive analysis provides novel insights into the regulatory networks of miRNAs and their targets, offering valuable contributions to our understanding of pepper's defense mechanisms against P. capsici.

High temperature and humidity in the environment disrupt bile acid metabolism, the gut microbiome, and GLP-1 secretion in mice.

High temperature and humidity in the environment are known to be associated with discomfort and disease, yet the underlying mechanisms remain unclear. We observed a decrease in plasma glucagon-like peptide-1 levels in response to high-temperature and humidity conditions. Through 16S rRNA gene sequencing, alterations in the gut microbiota composition were identified following exposure to high temperature and humidity conditions. Notably, changes in the gut microbiota have been implicated in bile acid synthesis. Further analysis revealed a decrease in lithocholic acid levels in high-temperature and humidity conditions. Subsequent in vitro experiments demonstrated that lithocholic acid increases glucagon-like peptide-1 secretion in NCI-H716 cells. Proteomic analysis indicated upregulation of farnesoid X receptor expression in the ileum. In vitro experiments revealed that the combination of lithocholic acid with farnesoid X receptor inhibitors resulted in a significant increase in GLP-1 levels compared to lithocholic acid alone. In this study, we elucidate the mechanism by which reduced lithocholic acid suppresses glucagon-like peptide 1 via farnesoid X receptor activation under high-temperature and humidity condition.

An improved model for target detection and pose estimation of a teleoperation power manipulator.

Introduction: A hot cell is generally deployed with a teleoperation power manipulator to complete tests, operations, and maintenance. The position and pose of the manipulator are mostly acquired through radiation-resistant video cameras arranged in the hot cell. In this paper, deep learning-based target detection technology is used to establish an experimental platform to test the methods for target detection and pose estimation of teleoperation power manipulators using two cameras. Methods: In view of the fact that a complex environment affects the precision of manipulator pose estimation, the dilated-fully convolutional one-stage object detection (dilated-FCOS) teleoperation power manipulator target detection algorithm is proposed based on the scale of the teleoperation power manipulator. Model pruning is used to improve the real-time performance of the dilated-FCOS teleoperation power manipulator target detection model. To improve the detection speed for the key points of the teleoperation power manipulator, the keypoint detection precision and model inference speed of different lightweight backbone networks were tested based on the SimpleBaseline algorithm. MobileNetv1 was selected as the backbone network to perform channel compression and pose distillation on the upsampling module so as to further optimize the inference speed of the model. Results and discussion: Compared with the original model, the proposed model was experimentally proven to reach basically the same precision within a shorter inference time (only 58% of that of the original model). The experimental results show that the compressed model basically retains the precision of the original model and that its inference time is 48% of that of the original model.

Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis.

Problems: Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. Aim: This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample. Methods: Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images. Results and conclusion: The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.

Active-targeting long-acting protein-glycopolymer conjugates for selective cancer therapy.

Non-fouling polymers are effective in improving the pharmacokinetics of therapeutic proteins, but short of biological functions for tumor targeting. In contrast, glycopolymers are biologically active, but usually have poor pharmacokinetics. To address this dilemma, herein we report in situ growth of glucose- and oligo(ethylene glycol)-containing copolymers at the C-terminal site of interferon alpha, an antitumor and antivirus biological drug, to generate C-terminal interferon alpha-glycopolymer conjugates with tunable glucose contents. The in vitro activity and in vivo circulatory half-life of these conjugates were found to decrease with the increase of glucose content, which can be ascribed to complement activation by the glycopolymers. Additionally, the cancer cell endocytosis of the conjugates was observed to maximize at a critical glucose content due to the tradeoff between complement activation and glucose transporter recognition by the glycopolymers. As a result, in mice bearing ovarian cancers with overexpressed glucose transporter 1, the conjugates with optimized glucose contents were identified to possess improved cancer-targeting ability, enhanced anticancer immunity and efficacy, and increased animal survival rate. These findings provided a promising strategy for screening protein-glycopolymer conjugates with optimized glucose contents for selective cancer therapy.

Clinical and humoral immune response characterization of SARS-CoV-2 Omicron BA.2.38 infection in pediatric patients.

Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a health concern for both unvaccinated and vaccinated individuals against coronavirus disease 2019 (COVID-19). To date, the humoral immune response following vaccination and natural infection remains uncharacterized in children ages 17 years and younger. To address this concern, we performed clinical and immunological analyses of IgM and IgG antibody responses to SARS-CoV-2 Omicron BA.2.38 infection in 64 pediatric patients. COVID-19 symptom severity decreased with age in pediatric patients, from 70.8% (17/24) in patients 0-2 years of age to 50% (6/12) and 50% (14/28) in patients 3-5 years and 6-17 years of age, respectively. Furthermore, fewer patients experienced symptoms when vaccinated with the CoronaVac or BBIBP-CorV vaccine (50%, 13/26) than unvaccinated patients (71%, 22/31). Using a protein array, we found that the Omicron BA.2.38 infection induced antibody responses to other Omicron variants (Omicron BA.1-BA.5), which increased with vaccination. Notably, non-Omicron and Omicron variants showed distinct serotypes. Altogether, our results provide insight into the clinical and immunological characteristics of pediatric patients with COVID-19 Omicron BA.2.38 who have and have not been vaccinated against COVID-19. These data may help develop more effective diagnostic tests and vaccines in the future.

Association between levels of IgG antibodies from vaccines and Omicron symptomatic infection among children and adolescents in China.

Background: Measurements of IgG antibodies to wild-type SARS-CoV-2 antigens can assess vaccine efficacy, but the absolute risk of Omicron symptomatic infection at different IgG levels for children and adolescents remains uncertain, as well as the minimum effective antibody level. We sought to determine the relationship between the tertiles of IgG antibodies to wild-type SARS-CoV-2 antigens and children with symptomatic infection of the pandemic and duration to negative conversion in China for the first time. Methods: A retrospective study was conducted, including 168 participants under 18 years old from the No.2 People's Hospital of Lanzhou, China, diagnosed with Omicron variant BA.2.38 between July 8, 2022, and August 2, 2022. We calculated odds ratios (OR) in univariate and multivariate regression to assess the association of symptomatic infection with the tertiles of IgG, respectively. Kaplan-Meier curves and Cox proportional hazards regression were used to evaluate the relationship between IgG level and negative conversion time. Results: The average age of the 168 children included in this study was 7.2 (4.7) years old, 133 (79.2%) were symptomatic patients, and the average negative conversion time was 12.2 (3.5) days. The participants with high IgG levels were less likely to become symptomatic, had a shorter turnaround time, and had higher values of IgM and nucleic acid CT. Compared to those with the lowest tertile of IgG, patients with the highest tertile had a 91% lower risk of developing a symptomatic infection after fully adjusting for confounders (OR = 0.09, 95% CI, 0.02-0.36, p = 0.001). There's no robust relationship between IgG level and negative conversion time in multivariate Cox regression. Conclusion: The risk of developing a symptomatic infection can be predicted independently by tertiles of IgG antibodies to wild-type SARS-CoV-2 antigens. High IgG levels can inhibit viral replication, vastly reduce the risk of symptomatic infections and promote a virus-negative conversion, especially when IgG quantitative detection was ≥3.44 S/CO, a potential threshold for protection and booster strategy in the future. More data and research are needed in the future to validate the predictive models.

Effectiveness of kumquat decoction for the improvement of cough caused by SARS-CoV-2 Omicron variants, a multicentre, prospective observational study.

BACKGROUND: Kumquat decoction is a traditional Chinese medicine formula and has been widely used to alleviate the coronavirus disease 2019 (COVID-19)-related cough in China. However, the effectiveness and safety of kumquat decoction remain unclear. PURPOSE: To assess the effectiveness and safety of kumquat decoction for COVID-19-related cough. STUDY DESIGN: A multicentre, prospective observational study. METHODS: We enrolled consecutive patients with mild-to-moderate COVID-19 from December 31, 2022, to January 3, 2023, during the Omicron phase in Yangshuo County, China. The primary outcome was the time from study baseline to sustained cough resolution by the last follow-up day on January 31, 2023. The effectiveness was evaluated by Cox proportional hazards models based on propensity score analyses. The secondary outcomes were the resolution of cough and other COVID-19-related symptoms by Days 3, 5, and 7. RESULTS: Of 1434 patients, 671 patients were excluded from the analysis of cough resolution. Among the remaining 763 patients, 481 (63.04%) received kumquat decoction, and 282 (36.96%) received usual care. The median age was 38.0 (interquartile range [IQR] 29.0, 50.0) years, and 55.7% were women. During a median follow-up of 7.000 days, 68.2% of patients in the kumquat group achieved sustained cough resolution (93.77 per 1000 person-days) compared to 39.7% in the usual care group (72.94 per 1000 person-days). The differences in restricted mean survival time (RMST) (kumquat decoction minus usual care group) for cough resolution were -0.742 days (95% CI, -1.235 to -0.250, P = 0.003) on Day 7. In the main analysis using propensity-score matching, the adjusted hazard ratio (HR) for cough resolution (kumquat decoction vs. usual care group) was 1.94 (95% CI, 1.48 to 2.53, P < 0.001). Similar findings were found in multiple sensitivity analyses. In addition, the use of kumquat decoction was associated with the resolution of cough, and a stuffy nose on Days 5 and 7, as well as the resolution of sore throat on Day 7 following medication. CONCLUSION: In this study among patients with COVID-19-related cough, receiving kumquat decoction was associated with an earlier resolution of cough symptoms.

The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage.

Background: Inflammatory mechanisms play important roles in intracerebral hemorrhage (ICH) and have been linked to the development of stroke-associated pneumonia (SAP). The neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) are inflammatory indexes that influence systemic inflammatory responses after stroke. In this study, we aimed to compare the predictive value of the NLR, SII, SIRI and PLR for SAP in patients with ICH to determine their application potential in the early identification of the severity of pneumonia. Methods: Patients with ICH in four hospitals were prospectively enrolled. SAP was defined according to the modified Centers for Disease Control and Prevention criteria. Data on the NLR, SII, SIRI and PLR were collected at admission, and the correlation between these factors and the clinical pulmonary infection score (CPIS) was assessed through Spearman's analysis. Results: A total of 320 patients were enrolled in this study, among whom 126 (39.4%) developed SAP. The results of the receiver operating characteristic (ROC) analysis revealed that the NLR had the best predictive value for SAP (AUC: 0.748, 95% CI: 0.695-0.801), and this outcome remained significant after adjusting for other confounders in multivariable analysis (RR=1.090, 95% CI: 1.029-1.155). Among the four indexes, Spearman's analysis showed that the NLR was the most highly correlated with the CPIS (r=0.537, 95% CI: 0.395-0.654). The NLR could effectively predict ICU admission (AUC: 0.732, 95% CI: 0.671-0.786), and this finding remained significant in the multivariable analysis (RR=1.049, 95% CI: 1.009-1.089, P=0.036). Nomograms were created to predict the probability of SAP occurrence and ICU admission. Furthermore, the NLR could predict a good outcome at discharge (AUC: 0.761, 95% CI: 0.707-0.8147). Conclusions: Among the four indexes, the NLR was the best predictor for SAP occurrence and a poor outcome at discharge in ICH patients. It can therefore be used for the early identification of severe SAP and to predict ICU admission.