Exploring the association between skin microbiota and inflammatory skin diseases: a two-sample Mendelian randomization analysis.

Dysbiosis in the skin microbiome is closely associated with various inflammatory skin diseases. However, current research on the causal relationship between the skin microbiome and inflammatory skin diseases lacks comprehensive and detailed investigation. We used a two-sample Mendelian randomization (MR) approach to explore associations between the skin microbiome and seven inflammatory skin diseases, including acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria. The GWAS summary data for the skin microbiome was derived from 647 participants in two German population-based cohorts, and for the inflammatory skin diseases, they were sourced from the FinnGen consortium. Our primary MR analysis method was the inverse variance weighted (IVW) method, complemented by alternatives like MR-Egger regression, weighted median estimation, and constrained maximum likelihood. Sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and MR-PRESSO outlier detection, were conducted to validate and stabilize our findings. We identified significant causal relationships between the skin microbiome and seven inflammatory skin diseases: acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria, with 7, 6, 9, 1, 7, 4, and 7 respective causal relationships for each disease. These relationships comprise 20 protective and 14 risk causal relationships. We applied the false discovery rate correction to these results. Sensitivity analysis revealed no significant pleiotropy or heterogeneity. Our study revealed both beneficial and detrimental causal relationships between diverse skin microbiota and inflammatory skin diseases. Additionally, the ecological niche of the skin microbiome was crucial to its functional impact. This research provided new insights into how skin microbiota impacted skin diseases and the development of therapeutic strategies.

Elucidating the Subcellular Localization of GLRaV-3 Proteins Encoded by the Unique Gene Block in N. benthamiana Suggests Implications on Plant Host Suppression.

Grapevine leafroll-associated virus 3 (GLRaV-3) is a formidable threat to the stability of the global grape and wine industries. It is the primary etiological agent of grapevine leafroll disease (GLD) and significantly impairs vine health, fruit quality, and yield. GLRaV-3 is a member of the genus Ampelovirus, Closteroviridae family. Viral genes within the 3' proximal unique gene blocks (UGB) remain highly variable and poorly understood. The UGBs of Closteroviridae viruses include diverse open reading frames (ORFs) that have been shown to contribute to viral functions such as the suppression of the host RNA silencing defense response and systemic viral spread. This study investigates the role of GLRaV-3 ORF8, ORF9, and ORF10, which encode the proteins p21, p20A, and p20B, respectively. These genes represent largely unexplored facets of the GLRaV-3 genome. Here, we visualize the subcellular localization of wildtype and mutagenized GLRaV-3 ORFs 8, 9, and 10, transiently expressed in Nicotiana benthamiana. Our results indicate that p21 localizes to the cytosol, p20A associates with microtubules, and p20B is trafficked into the nucleus to carry out the suppression of host RNA silencing. The findings presented herein provide a foundation for future research aimed at the characterization of the functions of these ORFs. In the long run, it would also facilitate the development of innovative strategies to understand GLRaV-3, mitigate its spread, and impacts on grapevines and the global wine industry.

Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.

Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.

Effect of trans-theoretical model-based nursing intervention on emotion and fear in post-liver cancer surgery patients.

OBJECTIVE: To investigate the efficacy of nursing interventions grounded in the trans-theoretical model on emotion and fear among patients undergoing surgery for hepatocellular carcinoma (HCC). METHODS: The study included 188 surgical patients from the Second People's Hospital of Lanzhou City who underwent HCC intervention between March 2020 and May 2022. The control group comprised 81 patients receiving standard postoperative care, while the observation group included 107 patients who received nursing interventions based on the trans-theoretical model. We assessed outcomes using the Fear of Progression Questionnaire-Short Form (FOP-Q-SF), Quality of Life Questionnaire Core 30 (QLQ-C30), Gastrointestinal Comfort Questionnaire (GCQ), Self-Rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS) before and after the intervention. Logistic regression was used to identify factors influencing post-intervention fear. RESULTS: Both groups showed improvement in FOP-Q-SF, QLQ-C30, GCQ, SAS, and SDS scores after the intervention. However, the observation group demonstrated significantly greater improvements (P < 0.05). There was a positive correlation between FOP-Q-SF scores and both SAS and SDS scores (all P < 0.05), and a negative correlation with QLQ-C30 and GCQ scores (both P < 0.05). Multifactorial logistic regression revealed that age (P < 0.001, OR: 8.328), gender (P < 0.001, OR: 0.181), literacy level (P < 0.001, OR: 0.354), and nursing care regimen (P < 0.001, OR: 0.078) were significant independent risk factors for persistence of fearpost-intervention. CONCLUSION: The implementation of nursing interventions based on the trans-theoretical model significantly reduces postoperative fear and anxiety, improves pain perception, and enhances overall comfort in patients after liver cancer surgery.

Geographical characterization of wines from seven regions of China by chemical composition combined with chemometrics: Quality characteristics of Chinese 'Marselan' wines.

This study investigated the basic and functional compositions, volatile compounds, intelligent sensory characteristics and antioxidant capacity of the commercial 'Marselan' wines from seven Chinese regions. The Nei Mongol wines featured high total reducing sugar, fructose, ammonia nitrogen, 17 monomeric phenolic acids contents and elevated antioxidant capacity. Malic acid was the only organic acid that significantly different in all seven regions. Malvidin-3-O-glucoside and trans-peonidin-3-O-(6-O-p-coumaryl)-glucoside showed the highest and lowest contents. A total of 102 volatiles was detected and Hebei wines had the most (91). Hexanoic acid and ß-damascenone were considered to have high potential sensory effects (OAV ≥ 1) as compounds detected in all regions. Floral, sweet, and fruity were the most important aroma series. E-eye analysis revealed the colors of the samples tended to yellowish with aging. PCA and OPLS-DA based on the basic wine composition, monomeric organic acids and anthocyanins allowed achieving a discrimination of the seven regions, respectively.

Metabolomics and molecular docking-directed anti-obesity study of the ethanol extract from Gynostemma pentaphyllum (Thunb.) Makino.

ETHNOPHARMACOLOGICAL RELEVANCE: Gynostemma pentaphyllum (Thunb.) Makino (G. pentaphyllum) is an oriental herb documented to treat many diseases, including obesity, hyperlipidemia, metabolic syndromes and aging. However, the anti-obesity mechanism of G. pentaphyllum remains poorly understood. AIM OF THE STUDY: To reveal the anti-obesity mechanism of G. pentaphyllum Extract (GPE) in High-Fat Diet (HFD)-induced obese mice through untargeted metabolomics, Real-Time Quantitative PCR (RT-qPCR), and immunohistochemical experiments. Additionally, to tentatively identify the active constituents through LC-MS/MS and molecular docking approaches. MATERIALS AND METHODS: GPE was prepared using ethanol reflux and purified by HP-20 macroporous resins. The components of GPE were identified by Liquid Chromatography- Mass Spectrometry (LC-MS) system. Forty-two C57BL/6 J mice were randomly and evenly divided into six groups, with seven mice in each group: the control group, obese model group, Beinaglutide group (positive control), and GPE low, medium, and high-dose groups (50 mg/kg, 100 mg/kg, and 200 mg/kg of 80% ethanol extract). Body weight, liver weight, blood glucose, blood lipids, and liver histopathological changes were assessed. Untargeted metabolomics was employed to characterize metabolic changes in obese mice after GPE treatment. The expression of genes related to differential metabolites was verified using Real-Time Quantitative PCR (RT-qPCR) and immunohistochemical experiments. The constituents with anti-obesity effects from GPE were tentatively identified through molecular docking approaches. RESULTS: A total of 17 compounds were identified in GPE. GPE significantly lowered body weight, total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) in obese mice and reduced liver weight and hepatic steatosis. Serum metabolomics identified 20 potential biomarkers associated with GPE treatment in obese mice, primarily related to tryptophan metabolism. GPE treatment downregulated the expression of Slc6a19 and Tph1 and upregulated Ucp1 expression. Molecular docking illustrated that compounds such as 20(R)-ginsenoside Rg3, Araliasaponin I, Damulin B, Gypenoside L, Oleifolioside B, and Tricin7-neohesperidoside identified in GPE exhibited favorable interaction with Tph1. CONCLUSION: The extract of G. pentaphyllum can inhibit the absorption of tryptophan and its conversion to 5-HT through the Slc6a19/Tph1 pathway, upregulating the expression of Ucp1, thereby promoting thermogenesis in brown adipose tissue, facilitating weight loss, and mitigating symptoms of fatty liver. Triterpenoids such as Araliasaponin I, identified in GPE, could be the potential inhibitor of Tph1 and responsible for the anti-obesity activities.

Explicit Image Caption Reasoning: Generating Accurate and Informative Captions for Complex Scenes with LMM.

The rapid advancement of sensor technologies and deep learning has significantly advanced the field of image captioning, especially for complex scenes. Traditional image captioning methods are often unable to handle the intricacies and detailed relationships within complex scenes. To overcome these limitations, this paper introduces Explicit Image Caption Reasoning (ECR), a novel approach that generates accurate and informative captions for complex scenes captured by advanced sensors. ECR employs an enhanced inference chain to analyze sensor-derived images, examining object relationships and interactions to achieve deeper semantic understanding. We implement ECR using the optimized ICICD dataset, a subset of the sensor-oriented Flickr30K-EE dataset containing comprehensive inference chain information. This dataset enhances training efficiency and caption quality by leveraging rich sensor data. We create the Explicit Image Caption Reasoning Multimodal Model (ECRMM) by fine-tuning TinyLLaVA with the ICICD dataset. Experiments demonstrate ECR's effectiveness and robustness in processing sensor data, outperforming traditional methods.

Integrated metabolomics and network pharmacology approach to exploring the anti-inflammatory mechanisms of Chuanwang xiaoyan capsules.

Chuanwang xiaoyan capsules (CWXYC) have anti-inflammatory and detoxification effect, are used in the treatment of acute and chronic tonsillitis, pharyngitis and other inflammation-related diseases clinically. However, the anti-inflammatory mechanisms have not been elucidated. This study aimed to investigate the anti-inflammatory mechanisms of CWXYC using cell metabolomics and network pharmacology strategy. Specifically, CWXYC could efficiently reduce the content of nitric oxide (NO), the cytokines Interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) in LPS-induced RAW264.7 cells. Furthermore, metabolomics was performed to achieve 23 differential metabolites and 9 metabolic pathways containing glutamate metabolism, glutathione metabolism, arginine and proline metabolism, urea cycle, malate-aspartate shuttle, phosphatidylcholine biosynthesis, transfer of acetyl groups into mitochondria, cysteine metabolism and ammonia recycling. The results of network pharmacology showed that CWXYC could treat inflammation through 10 active components, 10 key targets and 55 pathways. Then the results of molecular docking also approved that there existed strong binding energy between the active components and the key targets. Finally, metabolomics and network pharmacology were integrated to get core targets AKT1, SRC and EGFR. Western blot experiments verified that CWXYC could exert anti-inflammatory effect by down-regulating the activated Akt1 and Src proteins. This study demonstrated that CWXYC exerted effects against inflammation, and the potential mechanisms were elucidated. These novel findings will provide an important basis for further mechanism investigations.

Endothelial Heterogeneity in the Response to Autophagy Drives Small Vessel Muscularization in Pulmonary Hypertension.

BACKGROUND: Endothelial cell (EC) apoptosis and proliferation of apoptosis-resistant cells is a hallmark of pulmonary hypertension (PH). Yet, why some ECs die and others proliferate and how this contributes to vascular remodeling is unclear. We hypothesized that this differential response may: (1) relate to different EC subsets, namely pulmonary artery (PAECs) versus microvascular ECs (MVECs); (2) be attributable to autophagic activation in both EC subtypes; and (3) cause replacement of MVECs by PAECs with subsequent distal vessel muscularization. METHODS: EC subset responses to chronic hypoxia were assessed by single-cell RNA sequencing of murine lungs. Proliferative versus apoptotic responses, activation, and role of autophagy were assessed in human and rat PAECs and MVECs, and in precision-cut lung slices of wild-type mice or mice with endothelial deficiency in the autophagy-related gene 7 (Atg7EN-KO). Abundance of PAECs versus MVECs in precapillary microvessels was assessed in lung tissue from patients with PH and animal models on the basis of structural or surface markers. RESULTS: In vitro and in vivo, PAECs proliferated in response to hypoxia, whereas MVECs underwent apoptosis. Single-cell RNA sequencing analyses support these findings in that hypoxia induced an antiapoptotic, proliferative phenotype in arterial ECs, whereas capillary ECs showed a propensity for cell death. These distinct responses were prevented in hypoxic Atg7EN-KO mice or after ATG7 silencing, yet replicated by autophagy stimulation. In lung tissue from mice, rats, or patients with PH, the abundance of PAECs in precapillary arterioles was increased, and that of MVECs reduced relative to controls, indicating replacement of microvascular by macrovascular ECs. EC replacement was prevented by genetic or pharmacological inhibition of autophagy in vivo. Conditioned medium from hypoxic PAECs yet not MVECs promoted pulmonary artery smooth muscle cell proliferation and migration in a platelet-derived growth factor-dependent manner. Autophagy inhibition attenuated PH development and distal vessel muscularization in preclinical models. CONCLUSIONS: Autophagic activation by hypoxia induces in parallel PAEC proliferation and MVEC apoptosis. These differential responses cause a progressive replacement of MVECs by PAECs in precapillary pulmonary arterioles, thus providing a macrovascular context that in turn promotes pulmonary artery smooth muscle cell proliferation and migration, ultimately driving distal vessel muscularization and the development of PH.

Monosaccharide composition analysis by 2D quantitative gsHSQCi.

The physicochemical properties and biological activities of polysaccharides depend on their structures. Monosaccharide composition analysis is indispensable for the structural characterization of polysaccharides and is helpful in the quality control of polysaccharide preparation. Here, using a model mixture and tamarind seed polysaccharide as examples, we demonstrated that a quantitative 2D NMR method, gsHSQCi (three gradient-selective Heteronuclear Single Quantum Coherence spectra acquired with incremented repetition times, i = 1, 2, 3) can directly quantify a variety of monosaccharides in solution with adequate precision and accuracy, requiring no derivatization, postprocessing steps and column separation. Both anomeric and non-anomeric signals of monosaccharides can be utilized for content determination. More accurate quantification of fructose in a mixture containing nine monosaccharides is obtained, which is difficult to achieve by quantitative 1D 1HNMR and the common PMP-HPLC method (high-performance liquid chromatography through pre-column derivatization with 1-phenyl-3-methyl-5-pyrazolone) due to the peak overlapping and the poor derivatization efficiency, respectively. The results also revealed that Na[Fe(EDTA)] can serve as a proper relaxation-enhancing agent for saccharide samples to save experimental time. We expect that this approach can be applied as an alternative to analyzing the monosaccharide composition and be helpful in interpreting the structure of polysaccharides.

A Convenient In Situ Preparation of Cu2ZnSnS4-Anatase Hybrid Nanocomposite for Photocatalysis/Photoelectrochemical Water-Splitting Hydrogen Production.

This study details the rational design and synthesis of Cu2ZnSnS4 (CZTS)-doped anatase (A) heterostructures, utilizing earth-abundant elements to enhance the efficiency of solar-driven water splitting. A one-step hydrothermal method was employed to fabricate a series of CZTS-A heterojunctions. As the concentration of titanium dioxide (TiO2) varied, the morphology of CZTS shifted from floral patterns to sheet-like structures. The resulting CZTS-A heterostructures underwent comprehensive characterization through photoelectrochemical response assessments, optical measurements, and electrochemical impedance spectroscopy analyses. Detailed photoelectrochemical (PEC) investigations demonstrated notable enhancements in photocurrent density and incident photon-to-electron conversion efficiency (IPCE). Compared to pure anatase electrodes, the optimized CZTS-A heterostructures exhibited a seven-fold increase in photocurrent density and reached a hydrogen production efficiency of 1.1%. Additionally, the maximum H2 production rate from these heterostructures was 11-times greater than that of pure anatase and 250-times higher than the original CZTS after 2 h of irradiation. These results underscore the enhanced PEC performance of CZTS-A heterostructures, highlighting their potential as highly efficient materials for solar water splitting. Integrating Cu2ZnSnS4 nanoparticles (NPs) within TiO2 (anatase) heterostructures implied new avenues for developing earth-abundant and cost-effective photocatalytic systems for renewable energy applications.

Reducing Water Absorption and Improving Flexural Strength of Aluminosilicate Ceramics by MnO2 Doping.

As key performance indicators, the water absorption and mechanical strength of ceramics are highly associated with sintering temperature. Lower sintering temperatures, although favorable for energy saving in ceramics production, normally render the densification degree and water absorption of as-prepared ceramics to largely decline and increase, respectively. In the present work, 0.5 wt.% MnO2, serving as an additive, was mixed with aluminosilicate ceramics using mechanical stirring at room temperature, achieving a flexural strength of 58.36 MPa and water absorption of 0.05% and lowering the sintering temperature by 50 °C concurrently. On the basis of the results of TG-DSC, XRD, MIP, and XPS, etc., we speculate that the MnO2 additive promoted the elimination of water vapor in the ceramic bodies, effectively suppressing the generation of pores in the sintering process and facilitating the densification of ceramics at a lower temperature. This is probably because the MnO2 transformed into a liquid phase in the sintering process flows into the gap between grains, which removed the gas inside pores and filled the pores, suppressing the generation of pores and the abnormal growth of grains. This study demonstrated a facile and economical method to reduce the porosity and enhance the densification degree in the practical production of aluminosilicate ceramics.

Exploring the gut microbiota mediated biotransformation of Senecio scandens Buch.-Ham.: Insights from metabolite spectrum with UHPLC-Q-Orbitrap HRMS and bioinformatics analysis of gut microbiota metabolites.

Senecio scandens Buch.-Ham., a traditional Chinese medicine commonly used clinically, exhibits various pharmacological properties, including anti-inflammatory, anti-tumor, antiviral, and antibacterial activities. However, its water extracts' chemical components and metabolites are inadequately understood, limiting further research. In this study, the chemical components and metabolism processes of Senecio scandens, both in vivo (plasma, feces, urine, and bile) and in vitro (gut microbiota and liver microsomes), were characterized based on ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap high-resolution mass spectrometry. Additionally, metabolites detectable in fecal samples and intestinal microbiota incubated but absent in liver microsomes were identified as characteristic metabolites of intestinal microbiota. The targets of the characteristic metabolites of intestinal microbiota were collected, followed by exploration of potential pathways through KEGG enrichment analysis. As a result, a total of 133 chemical components were preliminarily identified, including 35 organic acids, 21 alkaloids, 19 flavonoids and their glycosides, 17 phenylpropanoids, 10 jacaranda ketones, and 31 other compounds. Notably, 12 of these were potentially novel compounds. In addition, 39 prototype components in rats and 109 metabolites were identified and characterized, including 102 in vivo and 52 metabolites in vitro (51 in rat gut microbiota and 24 in rat liver microsomes). The main metabolic pathways include oxidation, reduction, hydrolysis, methylation, glucuronidation, sulfonation, and acetylation reactions. Furthermore, KEGG enrichment analysis revealed that the characteristic metabolites of intestinal microbiota may be related to the ErbB, FoxO, mTOR, and MAPK signaling pathways, exhibiting anti-inflammatory and anti-tumor effects. In summary, the chemical components and metabolites of Senecio scandens were comprehensively identified using a rapid and accurate method, providing a scientific basis for the in-depth study of the material basis and its clinical application of Senecio scandens.

Pan-cancer prognostic model and immune microenvironment analysis of natural killer cell-related genes.

Background: Natural killer (NK) cells play a significant role in antitumor immunity and are closely related to tumor prognosis and recurrence. NK cell-based tumor immunotherapy, including immune checkpoint inhibition and CAR-engineered NK cells, is a promising area of research. However, there is a need for better NK cell-related models and associated biomarkers. Methods: The sequences of NK cell-related genes were obtained from the published NK cell CRISPR/Cas9 library data, and the common genes were selected as NK cell-related genes. The RNA sequencing (RNA-seq) and clinical data of 32 solid tumors from The Cancer Genome Atlas (TCGA) were downloaded from the UCSC Xena database, and the RNA-seq data of normal samples were downloaded from the Genotype-Tissue Expression (GTEx) database. The differentially expressed NK cell-related genes (DENKGs) between the tumor and normal samples were analyzed. The DENKGs related to the prognosis of solid tumors were selected via univariate Cox analysis, and 32 kinds of solid tumor prognostic models were constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox analysis. Survival, receiver operating characteristic (ROC), and independent prognostic analyses were employed to test the effectiveness of the model, along with a nomogram model and prediction curve. Differences in the immune pathways and microenvironment cells were analyzed between the high- and low-risk groups identified by the model. Results: We constructed a pan-cancer prognostic model with 63 NK cell-related genes and further identified DEPDC1 and ASPM as potentially offering new directions in tumor research by literature screening. Conclusions: In this study, 63 prognostic solid tumor markers were investigated using NK cell-related genes, and for the first time, a pan-cancer prognostic model was constructed to analyze their role in the immune microenvironment, which may contribute new insights into tumor research.

A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Role of Clinical Features, Pathogenic and Etiological Characteristics of Community-acquired Pneumonia with Type 2 Diabetes Mellitus in Early Diagnosis.

OBJECTIVE: To study the etiological characteristics of community-acquired pneumonia (CAP) combined with type 2 diabetes (T2D), providing a reference for early clinical diagnosis and treatment of the disease. METHODS: We selected a total of 93 patients with CAP and analyzed their metagenomics nextgeneration sequencing (mNGS) data. The case group comprised 46 patients with combined CAP/T2D, and the control group comprised 47 patients without diabetes. We analyzed the pathogenic findings of the two groups. RESULTS: There were statistically significant differences in age between the two groups (P = 0.001). Leukocytes (P = 0.012), blood platelets (P = 0.034), fibrinogen (P = 0.037), D-dimer (P = 0.000), calcitonin ogen (P = 0.015), ultrasensitive C-reactive protein or C-reactive protein (CRP) (P = 0.000), serum amyloid A (P = 0.000), and erythrocyte sedimentation rate (P = 0.003) were higher in the case group than in the control group. Albumin was lower in the case group than in the control group. All differences were statistically significant. The infection rates of Klebsiella pneumoniae (P = 0.030), Pseudomonas aeruginosa (P = 0.043), and Candida albicans (P = 0.032) were significantly different between the two groups. CONCLUSION: Compared with those without diabetes, the infection rates of Klebsiella pneumoniae, Pseudomonas aeruginosa, and Candida albicans were higher in patients with combined CAP/T2D.

Dietary supplements: clinical cholesterol-lowering efficacy and potential mechanisms of action.

This review aims to analyse the efficacy of dietary supplements in reducing plasma cholesterol levels. Focusing on evidence from meta-analyses of randomised controlled clinical trials, with an emphasis on potential mechanisms of action as supported by human, animal, and cell studies. Certain dietary supplements including phytosterols, berberine, viscous soluble dietary fibres, garlic supplements, soy protein, specific probiotic strains, and certain polyphenol extracts could significantly reduce plasma total and low-density lipoprotein (LDL) cholesterol levels by 3-25% in hypercholesterolemic patients depending on the type of supplement. They tended to be more effective in reducing plasma LDL cholesterol level in hypercholesterolemic individuals than in normocholesterolemic individuals. These supplements worked by various mechanisms, such as enhancing the excretion of bile acids, inhibiting the absorption of cholesterol in the intestines, increasing the expression of hepatic LDL receptors, suppressing the activity of enzymes involved in cholesterol synthesis, and activating the adenosine monophosphate-activated protein kinase signalling pathway.

Natural Product Quercetin-3-methyl ether Promotes Colorectal Cancer Cell Apoptosis by Downregulating Intracellular Polyamine Signaling.

Dysregulation of cellular metabolism is a key marker of cancer, and it is suggested that metabolism should be considered as a targeted weakness of colorectal cancer. Increased polyamine metabolism is a common metabolic change in tumors. Thus, targeting polyamine metabolism for anticancer therapy, particularly polyamine blockade therapy, has gradually become a hot topic. Quercetin-3-methyl ether is a natural compound existed in various plants with diverse biological activities like antioxidant and antiaging. Here, we reported that Quercetin-3-methyl ether inhibits colorectal cancer cell viability, and promotes apoptosis in a dose-dependent and time-dependent manner. Intriguingly, the polyamine levels, including spermidine and spermine, in colorectal cancer cells were reduced upon treatment of Quercetin-3-methyl ether. This is likely resulted from the downregulation of SMOX, a key enzyme in polyamine metabolism that catalyzes the oxidation of spermine to spermidine. These findings suggest Quercetin-3-methyl ether decreases cellular polyamine level by suppressing SMOX expression, thereby inducing colorectal cancer cell apoptosis. Our results also reveal a correlation between the anti-tumor activity of Quercetin-3-methyl ether and the polyamine metabolism modulation, which may provide new insights into a better understanding of the pharmacological activity of Quercetin-3-methyl ether and how it reprograms cellular polyamine metabolism.

Characterization of Fusarium solani Associated with Tobacco (Nicotiana tabacum) Root Rot in Henan, China.

The aim of this study was to characterize the Fusarium solani species complex (FSSC) population obtained from tobacco roots with root rot symptoms by morphological characteristics, molecular tests, and assessment of pathogenicity. Cultures isolated from roots were white to cream with sparse mycelium on potato dextrose agar, with colony growth of 21.5 ± 0.5 to 29.5 ± 0.5 mm after 3 days. Sporodochia were cream on carnation leaf agar (CLA) and Spezieller Nährstoffarmer agar (SNA), and macroconidia formed in sporodochia were 3 to 6 septate and straight to slightly curved, with wide central cells, a slightly short blunt apical cell, and a straight to almost cylindrical basal cell with a distinct foot shape, ranging in size from 20.92 to 64.37 × 3.91 to 6.57 µm. Microconidia formed on CLA were reniform and fusiform, with 0 or 1 to occasionally 2 septa, that formed on long monophialidic conidiogenous cells, with a size range of 5.99 to 32.32 × 1.76 to 5.84 µm. Globose to oval chlamydospores were smooth- to rough-walled, 6.5 to 13.3 ± 0.37 µm in diameter, and terminal or intercalary and occurred singly, in pairs, or occasionally in short chains on SNA. Molecular tests consisted of sequencing and phylogenetic analysis of the translation elongation factor-1 alpha (EF-1α), RNA polymerase II largest subunit, and second largest subunit regions. All the obtained sequences revealed 98.14 to 100% identity to F. solani in both Fusarium ID and Fusarium MLST databases. Phylogenetic trees of the EF-1α gene and concatenated three-locus data showed that isolates from tobacco in Henan grouped in the proposed group 5, which is nested within FSSC clade 3 (FSSC 5). Twenty-seven of the 28 isolates caused root rot in artificially inoculated tobacco seedlings, with a disease severity index ranging from 15.00 ± 1.67 to 91.11 ± 2.22. Cross-pathogenicity tests showed that three representative isolates were virulent to six species of Solanaceae and two species of Poaceae, with disease severity indexes ranging from 6.12 ± 0.56 to 84.44 ± 0.00, indicating that these isolates have a wide host range. The results may inform the control of tobacco root rot through improved crop rotations.

Insights into formation and stability mechanism of V7-type short amylose-resveratrol complex using molecular dynamics simulation and molecular docking.

Pre-formed V-type amylose as a kind of wall material has been reported to carry polyphenols, while the interaction mechanism between V-type amylose and polyphenol is still elusive. In this work, the formation and stability mechanism of a V7-type short amylose-resveratrol complex was investigated via isothermal titration calorimetry, molecular dynamics, and molecular docking. The results presented that two stoichiometric ratios of resveratrol to short amylose were calculated to 0.120 and 0.800, and the corresponding main driving force was hydrogen bonding and hydrophobic interaction, respectively. The folding and unfolding conformation of V7-type short amylose chains appeared alternately during the simulation. Resveratrol tended to be bound in the short amylose helix between 40 ns and 80 ns to form a more stable complex. Hydrogen bonds between resveratrol molecule and O6 at the 22nd glucose molecule/O2 at the 24th glucose molecules and hydrophobic interaction between resveratrol molecule and glucose molecules (19th, 20th, 21st and 23rd) could be found.