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Background Patients with critical illness due to infection with the 2019 coronavirus disease (COVID-19) show rapid disease progression to acute respiratory failure. The study aimed to screen the most useful predictive factor for critical illness caused by COVID-19. Methods The study prospectively involved 61 patients with COVID-19 infection as a derivation cohort, and 54 patients as a validation cohort. The predictive factor for critical illness was selected using LASSO regression analysis. A nomogram based on non-specific laboratory indicators was built to predict the probability of critical illness. Results The neutrophil-to-lymphocyte ratio (NLR) was identified as an independent risk factor for critical illness in patients with COVID-19 infection. The NLR had an area under receiver operating characteristic of 0.849 (95% confidence interval [CI], 0.707 to 0.991) in the derivation cohort and 0.867 (95% CI 0.747 to 0.944) in the validation cohort, the calibration curves fitted well, and the decision and clinical impact curves showed that the NLR had high standardized net benefit. In addition, the incidence of critical illness was 9.1% (1/11) for patients aged ≥ 50 and having an NLR < 3.13, and 50% (7/14) patients with age ≥ 50 and NLR ≥ 3.13 were predicted to develop critical illness. Based on the risk stratification of NLR according to age, this study has developed a COVID-19 pneumonia management process. Conclusions We found that NLR is a predictive factor for early-stage prediction of patients infected with COVID-19 who are likely to develop critical illness. Patients aged ≥ 50 and having an NLR ≥ 3.13 are predicted to develop critical illness, and they should thus have rapid access to an intensive care unit if necessary.

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.

Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON.

Given hepatitis B virus (HBV)-related hepatocellular carcinoma (HBV-HCC) exhibits unique gut microbiota characteristics and a significant immunosuppressive tumor microenvironment. Thus, a better understanding of the correlation between gut microbiota and the immunosuppressive response may help predict occurrence and prognosis of HBV-HCC. Here, in a cohort of ninety adults (healthy control n=30, HBV-cirrhosis n=30, HBV-HCC n=30) with clinical data, fecal 16S rRNA gene sequencing, matched peripheral blood immune response with flow cytometry analysis. Correlation between the gut microbiome of significantly different in HBV-HCC patients and clinical parameters as well as the peripheral immune response was assessed. We found that community structures and diversity of the gut microbiota in HBV-CLD patients become more unbalanced. Differential microbiota analysis that associated with inflammation were enriched. The beneficial bacteria of were decreased. Functional analysis of gut microbiota revealed that lipopolysaccharide biosynthesis, lipid metabolism, butanoate metabolism were significantly elevated in HBV-CLD patients. Spearman's correlation analysis showed that have positive correlation with CD3+T, CD4+T and CD8+T cell counts while negatively correlated with liver dysfunction. Furthermore, paired peripheral blood showed a decreased proportion of CD3+T, CD4+T and CD8+T cells, while an increased T (Treg) cells. The immunosuppressive response of programmed cell death 1 (PD-1), cytotoxic T-lymphocyte antigen 4 (CTLA-4), immune receptor tyrosine based inhibitor motor (ITIM) domain (TIGIT), T-cell immune domain, and multiple domain 3 (TIM-3) of CD8+T cells were higher in HBV-HCC patients. They were positively correlated with harmful bacteria, such as and . Our study indicated that gut beneficial bacteria, mainly and appeared dysbiosis in HBV-CLD patients. They have negative regulation of liver dysfunction and T cell immune response. It provides potential avenues for microbiome-based prevention and intervention for anti-tumor immune effects of HBV-CLD.

Hepatitis B virus (HBV)-related liver cirrhosis (HBV-LC) is a significant global health issue, affecting gut microbiota (GM) composition and metabolic processes. This study aimed to explore the associations between intestinal microbiota, metabolic profiles, and disease progression in patients with HBV-LC. Fecal samples were collected prospectively from 40 healthy controls (HC) and 83 HBV-LC patients between December 2022 and August 2023. Gut microbiota alterations at various stages of liver function were analyzed using 16S rRNA gene sequencing. Untargeted metabolomics was employed to identify potential biomarkers and metabolic pathways associated with early cirrhosis. Additionally, correlations between bacterial genera, inflammatory markers, and metabolites were investigated. HBV-LC patients demonstrated a significant reduction in bacterial diversity and relative abundance compared to the HC group. Genera such as and were notably depleted, while and were enriched in patients with Model for End-Stage Liver Disease (MELD) scores ≥ 21 or Child-Turcotte-Pugh C grade. Correlation analyses revealed strong associations between intestinal flora, clinical indicators of disease severity, and inflammatory factors. Metabolic analysis showed decreased levels of tocopherol and 21-hydroxypregnenolone, which were strongly linked to the reduced abundance of and . Biosynthesis of unsaturated fatty acids and linoleic acid metabolism emerged as critical enrichment pathways. HBV-LC patients displayed significant alterations in gut microbiota and fecal metabolites, which correlated closely with disease severity and inflammatory status. These findings provide new insights into cirrhosis pathogenesis and suggest potential biomarkers for early diagnosis and disease monitoring.

Dendrobium officinale is an edible medicinal herb. Its flowers contain various nutrients and active compounds, the abundance of which differs significantly across developmental stages. However, the molecular mechanisms underlying these variations remain poorly understood. This study employed combined transcriptomic and metabolomic analyses to investigate changes in gene expression and metabolite concentrations in Dendrobium flowers and buds. The analysis identified 2767 differentially expressed genes between the bud (group B) and flower (group F) stages, with 902 up-regulated and 1865 down-regulated in group B relative to group F. The number of differentially abundant secondary metabolites was 221, including 113 up-regulated and 108 down-regulated metabolites. The differential metabolites primarily consisted of lipids and lipid-like molecules, organic heterocyclic compounds, organic acids, and their derivatives, phenylacetones and polyketides, organic oxides, benzene ring-type compounds, hydrocarbons, and alkaloids with their derivatives. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the differentially expressed genes and metabolites revealed enrichment in pathways related to phytohormone signaling, phenylpropanoid biosynthesis, and zeaxanthin biosynthesis. Verification of 15 genes through quantitative real-time PCR (qRT-PCR) confirmed the accuracy of the transcriptomic analysis. Taken together, these findings contribute to an improved understanding of the nutritional and pharmacological characteristics of Dendrobium officinale flowers at different developmental stages.

Objective: Clinical trials have reported that Huaier granule inhibits the recurrence of hepatocellular carcinoma (HCC) after resection. However, its efficacy in patients at different clinical stages of HCC remains unknown. We investigated the effects of Huaier granule on the 3-year overall survival (OS) rate of patients at different clinical stages. Design: This cohort study included 826 patients with HCC, screened between January 2015 and December 2019. The patients were divided into Huaier (n = 174) and control groups (n = 652), and the 3-year OS rates were compared between the two groups. To eliminate bias caused by confounding factors, propensity score matching (PSM) was performed. We used the Kaplan-Meier method to estimate OS rate and tested the difference using the log-rank test. Results: Multivariable regression analysis revealed that Huaier therapy was an independent protective factor for 3-year survival rate. After PSM (1:2), the Huaier and control groups comprised 170 and 340 patients, respectively. The 3-year OS rate was remarkably higher in the Huaier group than in the control group (adjusted hazard ratio [aHR]: 0.36; 95% confidence interval: 0.26–0.49; p < 0.001). The aHR for Huaier use for 3–12, 12–24, and >24 months was 0.48, 0.23, and 0.16, respectively, indicating a dose-response pattern. For the 3–12-, 12–24-, and >24-month groups, the 3-year OS rate was 54.1%, 68.6%, and 90.4%, respectively. Multivariate stratified analysis confirmed that the mortality risk in Huaier users was lower than that in non-Huaier users in most subgroups. Conclusion: Adjuvant Huaier therapy improved the OS rate in patients with HCC. However, these findings require further verification through prospective clinical studies.

To gain better insight into the regulatory networks of anthocyanin biosynthesis, an integrated analysis of the metabolome and transcriptome in purple and green leaves of Tetrastigma hemsleyanum was conducted. Transcript and metabolite profiles were archived by RNA-sequencing data analysis and LC-ESI-MS/MS, respectively. There were 209 metabolites and 4211 transcripts that were differentially expressed between purple and green leaves. Correlation tests of anthocyanin contents and transcriptional changes showed 141 significant correlations (Pearson correlation coefficient >0.8) between 16 compounds and 14 transcripts involved in the anthocyanin biosynthesis pathway. Some novel genes and metabolites were discovered as potential candidate targets for the improvement of anthocyanin content and superior cultivars.

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

Over 50% of patients with hepatitis B virus‐associated hepatocellular carcinoma (HBV‐HCC) are diagnosed at an advanced stage, which is characterized by immune imbalance between CD8+ T cells and regulatory T (Treg) cells that accelerates disease progression. However, there is no imbalance indicator to predict clinical outcomes. Here, we show that the proportion of CD8+ T cells decreases and Treg cells increases in advanced HBV‐HCC patients. During this stage, CD8+ T cells and Treg cells expressed the coinhibitory molecule PD‐1 and the costimulatory molecule ICOS, respectively. Additionally, the ratio between PD‐1+CD8 and ICOS+Tregs showed significant changes. Patients were further divided into high‐ and low‐ratio groups: PD‐1+CD8 and ICOS+Tregs high‐ (PD‐1/ICOShi) and low‐ratio (PD‐1/ICOSlo) groups according to ratio median. Compared with PD‐1/ICOSlo patients, the PD‐1/ICOShi group had better clinical prognosis and weaker CD8+ T cells exhaustion, and the T cell‐killing and proliferation functions were more conservative. Surprisingly, the small sample analysis found that PD‐1/ICOShi patients exhibited a higher proportion of tissue‐resident memory T (TRM) cells and had more stable killing capacity and lower apoptosis capacity than PD‐1/ICOSlo advanced HBV‐HCC patients treated with immune checkpoint inhibitors (ICIs). In conclusion, the ratio between PD‐1+CD8 and ICOS+Tregs was associated with extreme immune imbalance and poor prognosis in advanced HBV‐HCC. These findings provide significant clinical implications for the prognosis of advanced HBV‐HCC and may serve as a theoretical basis for identifying new targets in immunotherapy. Imbalance between CD8+ T cells and regulatory T (Treg) cells accelerates disease progression, but there is no imbalance indicator to predict clinical outcomes. The ratio between PD‐1+CD8 and ICOS+Tregs was associated with extreme immune imbalance and poor prognosis in advanced hepatitis B virus‐associated hepatocellular carcinoma (HBV‐HCC).