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This study examines the nonlinear dynamic response of sandwich plates under low-velocity impacts (LVIs), which consist of a porous aluminum foam core with functionally graded (FG) graphene platelets (GPLs) reinforcement and two titanium alloy face sheets. The finite element method is employed to perform the numerical simulations and the Hertz contact theory is used to model the impact behavior. The generic Halpin-Tsai model, which incorporates the porosity effect, is adopted to estimate the mechanical properties of core made of the FG-GPLRC, based on the experimental data of the closed-cell aluminum alloy foam. The simulation results reveal that the FG-X configuration has the highest impact resistance, and that the addition of GPLs improves the impact performance. Moreover, a thicker core can reduce both the first contact time and the vibration period of plates, while a looser boundary condition can prolong the vibration time after the first impact. Additionally, a higher impactor velocity, mass and radius lead to a higher deflection and contact force. The oblique impact decreases the impact energy. This work provides a lighter and stiffer design for structure engineers when components suffer low-velocity impact.

ObjectiveThis retrospective cohort study aimed to investigate the composition and diversity of lung microbiota in patients with severe pneumonia and explore its association with short-term prognosis.MethodsA total of 301 patients diagnosed with severe pneumonia underwent bronchoalveolar lavage fluid metagenomic next-generation sequencing (mNGS) testing from February 2022 to January 2024. After applying exclusion criteria, 236 patients were included in the study. Baseline demographic and clinical characteristics were compared between survival and non-survival groups. Microbial composition and diversity were analyzed using alpha and beta diversity metrics. Additionally, LEfSe analysis and machine learning methods were employed to identify key pathogenic microorganism associated with short-term mortality. Microbial interaction modes were assessed through network co-occurrence analysis.ResultsThe overall 28-day mortality rate was 37.7% in severe pneumonia. Non-survival patients had a higher prevalence of hypertension and exhibited higher APACHE II and SOFA scores, higher procalcitonin (PCT), and shorter hospitalization duration. Microbial α and β diversity analysis showed no significant differences between the two groups. However, distinct species diversity patterns were observed, with the non-survival group showing a higher abundance of Acinetobacter baumannii, Klebsiella pneumoniae, and Enterococcus faecium, while the survival group had a higher prevalence of Corynebacterium striatum and Enterobacter. LEfSe analysis identified 29 distinct terms, with 10 potential markers in the non-survival group, including Pseudomonas sp. and Enterococcus durans. Machine learning models selected 16 key pathogenic bacteria, such as Klebsiella pneumoniae, significantly contributing to predicting short-term mortality. Network co-occurrence analysis revealed greater complexity in the non-survival group compared to the survival group, with differences in central genera.ConclusionOur study highlights the potential significance of lung microbiota composition in predicting short-term prognosis in severe pneumonia patients. Differences in microbial diversity and composition, along with distinct microbial interaction modes, may contribute to variations in short-term outcomes. Further research is warranted to elucidate the clinical implications and underlying mechanisms of these findings.

The gene family of thaumatin-like proteins (TLPs) plays a crucial role in the adaptation of organisms to environmental stresses. In recent years, fungal secreted proteins (SP) with inducing disease resistance activity in plants have emerged as important elicitors in the control of fungal diseases. Identifying SPs with inducing disease resistance activity and studying their mechanisms are crucial for controlling sheath blight. In the present study, 10 proteins containing the thaumatin-like domain were identified in strain AG4-JY of Rhizoctonia solani and eight of the 10 proteins had signal peptides. Analysis of the TLP genes of the 10 different anastomosis groups (AGs) showed that the evolutionary relationship of the TLP gene was consistent with that between different AGs of R. solani. Furthermore, it was found that RsTLP3, RsTLP9 and RsTLP10 were regarded as secreted proteins for their signaling peptides exhibited secretory activity. Prokaryotic expression and enzyme activity analysis revealed that the three secreted proteins possess glycoside hydrolase activity, suggesting they belong to the TLP family. Additionally, spraying the crude enzyme solution of the three TLP proteins could enhance maize resistance to sheath blight. Further analysis showed that genes associated with the salicylic acid and ethylene pathways were up-regulated following RsTLP3 application. The results indicated that RsTLP3 had a good application prospect in biological control.

In the current study, Cnicus benedictus extract was loaded into electrospun gelatin scaffolds for diabetic wound healing applications. Scaffolds were characterized in vitro by mechanical testing, cell culture assays, electron microscopy, cell migration assay, and antibacterial assay. In vivo wound healing study was performed in a rat model of diabetic wound. In vitro studies revealed fibrous architecture of our developed dressings and their anti-inflammatory properties. In addition, Cnicus benedictus extract- loaded wound dressings prevented bacterial penetration. In vivo study showed that wound size reduction, collagen deposition, and epithelial thickness were significantly greater in Cnicus benedictus extract-loaded scaffolds than other groups. Gene expression studies showed that the produced wound dressings significantly upregulated VEGF and IGF genes expression in diabetic wounds.

Abstract Sepsis is a serious complication of liver cirrhosis. This study aimed to develop a risk prediction model for sepsis among patients with liver cirrhosis. A total of 3130 patients with liver cirrhosis were enrolled from the Medical Information Mart for Intensive Care IV database, and randomly assigned into training and validation cohorts in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables and select predictor variables. Multivariate logistic regression was used to establish the prediction model. Based on LASSO and multivariate logistic regression, gender, base excess, bicarbonate, white blood cells, potassium, fibrinogen, systolic blood pressure, mechanical ventilation, and vasopressor use were identified as independent risk variables, and then a nomogram was constructed and validated. The consistency index (C‐index), receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. As a result of the nomogram, good discrimination was achieved, with C‐indexes of 0.814 and 0.828 for the training and validation cohorts, respectively, and an area under the curve of 0.849 in the training cohort and 0.821 in the validation cohort. The calibration curves demonstrated good agreement between the predictions and observations. The DCA curves showed the nomogram had significant clinical value. We developed and validated a risk‐prediction model for sepsis in patients with liver cirrhosis. This model can assist clinicians in the early detection and prevention of sepsis in patients with liver cirrhosis.

Setosphaeria turcica, the fungal pathogen responsible for northern corn leaf blight in maize, forms specialized infectious structures called appressoria that are critical for fungal penetration of maize epidermal cells. The Rab family of proteins play a crucial role in the growth, development, and pathogenesis of many eukaryotic species. Rab4, in particular, is a key regulator of endocytosis and vesicle trafficking, essential for filamentous growth and successful infection by other fungal pathogens. In this study, we silenced StRAB4 in S. turcica to gain a better understanding the function of Rab4 in this plant pathogen. Phenotypically, the mutants exhibited a reduced growth rate, a significant decline in conidia production, and an abnormal conidial morphology. These phenotypes indicate that StRab4 plays an instrumental role in regulating mycelial growth and conidial development in S. turcica. Further investigations revealed that StRab4 is a positive regulator of cell wall integrity and melanin secretion. Functional enrichment analysis of differentially expressed genes highlighted primary enrichments in peroxisome pathways, oxidoreductase and catalytic activities, membrane components, and cell wall organization processes. Collectively, our findings emphasize the significant role of StRab4 in S. turcica infection and pathogenicity in maize and provide valuable insights into fungal behavior and disease mechanisms.

The rapid development of wearable electronics requires its energy supply part to be flexible, wearable, integratable and sustainable. However, some of the energy supply units cannot meet these requirements at the same time, and there is also a capacity limitation of the energy storage units, and the development of sustainable wearable self-charging power supplies is crucial. Here, we report a wearable sustainable energy harvesting-storage hybrid self-charging power textile. The power textile consists of a coaxial fiber-shaped polylactic acid/reduced graphene oxide/polypyrrole (PLA-rGO-PPy) triboelectric nanogenerator (fiber-TENG) that can harvest low-frequency and irregular energy during human motion as a power generation unit, and a novel coaxial fiber-shaped supercapacitor (fiber-SC) prepared by functionalized loading of a wet-spinning graphene oxide fiber as an energy storage unit. The fiber-TENG is flexible, knittable, wearable and adaptable for integration with various portable electronics. The coaxial fiber-SC has high volumetric energy density and good cycling stability. The fiber-TENG and fiber-SC are flexible yarn structures for wearable continuous human movement energy harvesting and storage as on-body self-charging power systems, with light-weight, ease of preparation, great portability and wide applicability. The integrated power textile can provide an efficient route for sustainable working of wearable electronics.

ABSTRACTThe length of stay (LOS) in hospital varied considerably in different patients with COVID-19 caused by SARS-CoV-2 Omicron variant. The study aimed to explore the clinical characteristics of Omicron patients, identify prognostic factors, and develop a prognostic model to predict the LOS of Omicron patients. This was a single center retrospective study in a secondary medical institution in China. A total of 384 Omicron patients in China were enrolled. According to the analyzed data, we employed LASSO to select the primitive predictors. The predictive model was constructed by fitting a linear regression model using the predictors selected by LASSO. Bootstrap validation was used to test performance and eventually we obtained the actual model. Among these patients, 222 (57.8%) were female, the median age of patients was 18 years and 349 (90.9%) completed two doses of vaccination. Patients on admission diagnosed as mild were 363 (94.5%). Five variables were selected by LASSO and a linear model, and those with P < 0.05 were integrated into the analysis. It shows that if Omicron patients receive immunotherapy or heparin, the LOS increases by 36% or 16.1%. If Omicron patients developed rhinorrhea or occur familial cluster, the LOS increased by 10.4% or 12.3%, respectively. Moreover, if Omicron patients’ APTT increased by one unit, the LOS increased by 0.38%. Five variables were identified, including immunotherapy, heparin, familial cluster, rhinorrhea, and APTT. A simple model was developed and evaluated to predict the LOS of Omicron patients. The formula is as follows: Predictive LOS = exp(1*2.66263 + 0.30778*Immunotherapy + 0.1158*Familiar cluster + 0.1496*Heparin + 0.0989*Rhinorrhea + 0.0036*APTT).

Summary: Background: Severe eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP) remains the most relapsed subtype of uncontrolled CRSwNP. CM310, a humanised anti-interleukin (IL)-4 receptor alpha monoclonal antibody, inhibits IL-4 and IL-13 signaling which underlying eosinophilic inflammation. This study aims to evaluate the efficacy and safety of CM310 in patients with severe ECRSwNP. Methods: A multicentre, randomised, double-blind, and placebo-controlled phase 2 clinical trial was conducted. 56 eligible adult patients with severe ECRSwNP were randomised 1:1 to receive subcutaneously either CM310 (300 mg) or placebo every 2 weeks under the background therapy of mometasone furoate nasal spray (MFNS) for 16 weeks, with 8 weeks of follow-up. Coprimary endpoints included the changes from baseline in nasal polyp score (NPS) and nasal congestion score (NCS) at week 16. Key secondary endpoints included sinus Lund-Mackay CT score, change in sinus volume occupied by disease, University of Pennsylvania Smell Identification Test score, 22-item Sino-nasal Outcome Test score, and total symptom score. Safety, pharmacodynamics, and changes in type 2 inflammation biomarkers were assessed. This study is registered with ClinicalTrials.gov, NCT04805398. Findings: Between April 6, 2021, and March 18, 2022, 27 patients respectively in both the CM310 and placebo groups completed the study. Findings suggested that CM310 improved the coprimary efficacy endpoints of decreasing nasal polyp size and alleviating nasal congestion compared with the placebo. Least squares (LS) mean differences (CM310 vs placebo) of change from baseline in NPS and NCS at week 16 were −2.1 (95% CI −2.9, −1.4; p

Purpose: We evaluated the association between ambient particulate matter (PM) exposure and eosinophilic chronic rhinosinusitis with nasal polyps (CRSwNP), and predicted the CRSwNP recurrence risk using machine learning algorithms. Methods: In total, 1,086 patients with CRSwNP were recruited from nine hospitals in China during 2014–2019. The average annual concentrations of ambient PMs before surgery were assessed using satellite-based daily concentrations of PM2.5 and PM10 for a 1 × 1-km2 area. Linear regression and logistic regression models were used to evaluate the associations of PM exposure with eosinophilia and risks of eosinophilic CRSwNPs. In addition, mediation effect analysis was used to validate the interrelationships of the aforementioned factors. Finally, machine learning algorithms were used to predict the recurrence risks of CRSwNPs. Results: There was a significantly increased risk of eosinophilic CRSwNPs with each 10 μg/m3 increase in PMs, with odds ratios (ORs) of 1.039 (95% confidence interval [CI] = 1.007–1.073) for PM10 and 1.058 (95% CI = 1.007– 1.112) for PM2.5. Eosinophils had a significant mediation effect, which accounted for 52% and 35% of the relationships of CRSwNP recurrence with PM10 and PM2.5, respectively. Finally, we developed a naïve Bayesian model to predict the risk of CRSwNP recurrence based on PM exposure, inflammatory data, and patients’ demographic factors. Conclusions: Increased PM exposure is associated with an increased risk of eosinophilic CRSwNP in China. Therefore, patients with eosinophilic CRSwNP should reduce PM exposure to mitigate its harmful impacts.

Abstract Letter handwriting, especially stroke correction, is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the public. In this study, a biodegradable and conductive carboxymethyl chitosan-silk fibroin (CSF) film is prepared to design wearable triboelectric nanogenerator (denoted as CSF-TENG), which outputs of V oc ≈ 165 V, I sc ≈ 1.4 μA, and Q sc ≈ 72 mW cm−2. Further, in vitro biodegradation of CSF film is performed through trypsin and lysozyme. The results show that trypsin and lysozyme have stable and favorable biodegradation properties, removing 63.1% of CSF film after degrading for 11 days. Further, the CSF-TENG-based human–machine interface (HMI) is designed to promptly track writing steps and access the accuracy of letters, resulting in a straightforward communication media of human and machine. The CSF-TENG-based HMI can automatically recognize and correct three representative letters (F, H, and K), which is benefited by HMI system for data processing and analysis. The CSF-TENG-based HMI can make decisions for the next stroke, highlighting the stroke in advance by replacing it with red, which can be a candidate for calligraphy practice and correction. Finally, various demonstrations are done in real-time to achieve virtual and real-world controls including writing, vehicle movements, and healthcare.

Abstract Background Although 20–60% of patients with chronic rhinosinusitis with nasal polyps (CRSwNP) have asthma, the risk factors associated with comorbid asthma are not clear. The aim of the study was to investigate the factors associated with asthma, and develop a practical scoring system to screen asthma comorbidity in CRSwNP patients. Methods This report describes a cross-sectional study with consecutive CRSwNP patients. Two cohorts of CRSwNP patients named “modelling” group and “validation” group were investigated respectively. Logistic regression analysis was performed based on demographic and clinical data collected from patients in the modelling group to determine the risk factors associated with asthma, and establish a scoring system for screening comorbid asthma. Receiver operating characteristic curve was constructed to evaluate the screening system; the optimal cut-off point was established by means of the Yoden Index. The consistency between the diagnosis of asthma by the Global Initiative for Asthma (GINA) criteria and by the screening system was assessed by Kappa value in the validation group. Results Totally 150 patients in modelling group and 78 patients in validation group were enrolled. Female gender (odds ratio [OR] = 6.4; P

Salinity stress is one of the major abiotic stresses affecting crop growth and production. Rice is an important food crop in the world, but also a salt-sensitive crop, and the rice seedling stage is the most sensitive to salt stress, which directly affects the final yield formation. In this study, two RIL populations derived from the crosses of CD (salt-sensitive)/WD (salt-tolerant) and KY131 (salt-sensitive)/XBJZ (salt-tolerant) were used as experimental materials, and the score of salinity toxicity (SST), the relative shoot length (RSL), the relative shoot fresh weight (RSFW), and the relative shoot dry weight (RSDW) were used for evaluating the degree of tolerance under salt stress in different lines. The genetic linkage map containing 978 and 527 bin markers were constructed in two RIL populations. A total of 14 QTLs were detected on chromosomes 1, 2, 3, 4, 7, 9, 10, 11, and 12. Among them, qSST12-1, qSST12-2, and qRSL12 were co-localized in a 140-kb overlap interval on chromosome 12, which containing 16 candidate genes. Furthermore, transcriptome sequencing and qRT-PCR were analyzed in CD and WD under normal and 120 mM NaCl stress. LOC_Os12g29330, LOC_Os12g29350, LOC_Os12g29390, and LOC_Os12g29400 were significantly induced by salt stress in both CD and WD. Sequence analysis showed that LOC_Os12g29400 in the salt-sensitive parents CD and KY131 was consistent with the reference sequence (Nipponbare), whereas the salt-tolerant parents WD and XBJZ differed significantly from the reference sequence both in the promoter and exon regions. The salt-tolerant phenotype was identified by using two T3 homozygous mutant plants of LOC_Os12g29400; the results showed that the score of salinity toxicity (SST) of the mutant plants (CR-3 and CR-5) was significantly lower than that of the wild type, and the seedling survival rate (SSR) was significantly higher than that of the wild type, which indicated that LOC_Os12g29400 could negatively regulate the salinity tolerance of rice at the seedling stage. The results lay a foundation for the analysis of the molecular mechanism of rice salinity tolerance and the cultivation of new rice varieties.

Improving rice yield is one of the most important food issues internationally. It is an undeniable goal of rice breeding, and the effective panicle number (EPN) is a key factor determining rice yield. Increasing the EPN in rice is a major way to increase rice yield. Currently, the main quantitative trait locus (QTL) for EPN in rice is limited, and there is also limited research on the gene for EPN in rice. Therefore, the excavation and analysis of major genes related to EPN in rice is of great significance for molecular breeding and yield improvement. This study used japonica rice varieties Dongfu 114 and Longyang 11 to construct an F5 population consisting of 309 individual plants. Two extreme phenotypic pools were constructed by identifying the EPN of the population, and QTL-seq analysis was performed to obtain three main effective QTL intervals for EPN. This analysis also helped to screen out 34 candidate genes. Then, EPN time expression pattern analysis was performed on these 34 genes to screen out six candidate genes with higher expression levels. Using a 3K database to perform haplotype analysis on these six genes, we selected haplotypes with significant differences in EPN. Finally, five candidate genes related to EPN were obtained.

Abstract Background Characterised by feelings of helplessness in the face of clinical, organization and societal demands, medical students are especially prone to moral distress (MD). Despite risks of disillusionment and burnout, efforts to support them have been limited by a dearth of data and understanding of MD in medical students. Yet, new data on how healthcare professionals confront difficult care situations suggest that MD could be better understood through the lens of the Ring Theory of Personhood (RToP). A systematic scoping review (SSR) guided by the RToP is proposed to evaluate the present understanding of MD amongst medical students. Methods The Systematic Evidence-Based Approach (SEBA) is adopted to map prevailing accounts of MD in medical students. To enhance the transparency and reproducibility, the SEBA methodology employs a structured search approach, concurrent and independent thematic analysis and directed content analysis (Split Approach), the Jigsaw Perspective that combines complementary themes and categories, and the Funnelling Process that compares the results of the Jigsaw Perspective with tabulated summaries to ensure the accountability of these findings. The domains created guide the discussion. Results Two thousand six hundred seventy-one abstracts were identified from eight databases, 316 articles were reviewed, and 20 articles were included. The four domains identified include definitions, sources, recognition and, interventions for MD. Conclusions MD in medical students may be explained as conflicts between the values, duties, and principles contained within the different aspects of their identity. These conflicts which are characterised as disharmony (within) and dyssynchrony (between) the rings of RToP underline the need for personalised and longitudinal evaluations and support of medical students throughout their training. This longitudinal oversight and support should be supported by the host organization that must also ensure access to trained faculty, a nurturing and safe environment for medical students to facilitate speak-up culture, anonymous reporting, feedback opportunities and supplementing positive role modelling and mentoring within the training program.

Labeling of the connected components is the key operation of the target recognition and segmentation in remote sensing images. The conventional connected-component labeling (CCL) algorithms for ordinary optical images are considered time-consuming in processing the remote sensing images because of the larger size. A dynamic run-length based CCL algorithm (DyRLC) is proposed in this paper for the large size, big granularity sparse remote sensing image, such as space debris images and ship images. In addition, the equivalence matrix method is proposed to help design the pre-processing method to accelerate the equivalence labels resolving. The result shows our algorithm outperforms 22.86% on execution time than the other algorithms in space debris image dataset. The proposed algorithm also can be implemented on the field programming logical array (FPGA) to enable the realization of the real-time processing on-board.

Hepatocellular carcinoma (HCC), which has become one of the most significant malignancies causing cancer-related mortality, presents genetic and phenotypic heterogeneity that makes predicting prognosis challenging. Aging-related genes have been increasingly reported as significant risk factors for many kinds of malignancies, including HCC. In this study, we comprehensively dissected the features of transcriptional aging-relevant genes in HCC from multiple perspectives. We applied public databases and self-consistent clustering analysis to classify patients into C1, C2, and C3 clusters. The C1 cluster had the shortest overall survival time and advanced pathological features. Least absolute shrinkage and selection operator (LASSO) regression analysis was adopted to build the prognostic prediction model based on six aging-related genes (HMMR, S100A9, SPP1, CYP2C9, CFHR3, and RAMP3). These genes were differently expressed in HepG2 cell lines compared with LO2 cell lines measured by the mRNA expression level. The high-risk score group had significantly more immune checkpoint genes, higher tumor immune dysfunction and exclusion score, and stronger chemotherapy response. The results indicated that the age-related genes have a close correlation with HCC prognosis and immune characteristics. Overall, the model based on six aging-associated genes demonstrated great prognostic prediction ability.

Robust assays to quantify adeno-associated virus (AAV) vector expression and potency are essential for gene therapy development. These assays inform the efficacy, safety, and pharmacodynamic profiles of AAV development candidates. Additionally, for gene downregulation strategies such as RNAi, knockdown of endogenous genes reflects the mechanism of action of such development candidates. Therefore, a method to quantify target mRNA repression is necessary for measuring vector potency both in vitro and in vivo. Here, we report the development of a one-step reverse-transcription droplet digital PCR (RT-ddPCR) method to analyze expression of AAV vectors and the potency of AAV-RNAi vectors. This one-step RT-ddPCR method simplifies the workflow, allows for duplexing reactions, and enables absolute quantification of transcripts without standard materials. With a gene augmentation vector, we demonstrate the application of RT-ddPCR in quantifying vector expression in vitro and in non-human primate (NHP) samples. This novel method is demonstrated to be precise and linear within the range of 0.05–25 ng of RNA input. Using an AAV-RNAi vector, we further demonstrate the utility of this RT-ddPCR method in quantifying potency. Orthogonal potency assays, including ELISA and functional readout, correlate well with RT-ddPCR results. Therefore, one-step RT-ddPCR can be implemented in the analytical and pharmacological characterization of AAV vectors.

Abstract Cancer immunotherapy is a promising therapeutic approach, but the prognostic value of immune-related genes in osteosarcoma (OS) is unknown. Here, Target-OS RNA-seq data were analyzed to detect differentially expressed genes (DEGs) between OS subgroups, followed by functional enrichment analysis. Cox proportional risk regression was performed for each immune-related gene, and a risk score model to predict the prognosis of patients with OS was constructed. The risk scores were calculated using the risk signature to divide the training set into high-risk and low-risk groups, and validation was performed with GSE21257. We identified two immune-associated clusters, C1 and C2. C1 was closely related to immunity, and the immune score was significantly higher in C1 than in C2. Furthermore, we validated 6 immune cell hub genes related to the prognosis of OS: CD8A, KIR2DL1, CD79A, APBB1IP, GAL, and PLD3. Survival analysis revealed that the prognosis of the high-risk group was significantly worse than that of the low-risk group. We also explored whether the 6-gene prognostic risk model was effective for survival prediction. In conclusion, the constructed a risk score model based on immune-related genes and the survival of patients with OS could be a potential tool for targeted therapy.

BackgroundThe continued ‘evolution’ of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of the Omicron variant after the Delta variant, resulting in a significant increase in the number of people with COVID-19. This increase in the number of cases continues to have a significant impact on lives. Therefore, a more detailed understanding of the clinical characteristics of Omicron infection is essential.MethodsUsing medical charts, we extracted clinical information for 384 patients infected with the Omicron variant in Anyang City, Henan Province, China. Epidemiology and clinical characteristics were compared with a cohort of people infected with the Delta variant in Zhengzhou in 2021.FindingsCommon initial symptoms at onset of illness were cough [240 (63%)], expectoration [112 (29%)], fever [96 (25%)], nasal congestion [96 (25%)] and myalgia or fatigue [30 (6%)]. In patients with the Omicron variant, levels of total cholesterol, low-density lipoprotein and creatinine increased in 52 (14%), 36 (9%) and 58 (15%) patients, respectively, compared with patients with the Delta variant [one (1%), one (1%) and two (2%)]. Levels of triglyceride and high-density lipoprotein also increased. In patients with the Omicron variant, the levels of specific gravity and the erythrocyte sedimentation rate were increased in 115 (30%) and 81 (21%) patients, and serum levels of complement 3 decreased in 93 (41%).ResultsCompared with patients infected with Delta, no major differences in initial clinical symptoms were identified in patients infected with Omicron. However, dyslipidemia and kidney injury were much more severe in patients with the Omicron variant, and the erythrocyte sedimentation rate was increased. Due to decreased levels of complement 3, the immunity of patients with the Omicron variant was weak.