The effect of Huanglian Jiedu Decoction on the intestinal flora of type 2 diabetes mellitus(T2DM) was investigated using 16S rRNA sequencing technology. Sixty rats were randomly divided into a normal group(10 rats) and a modeling group(50 rats). After one week of adaptive feeding, a high-fat diet + streptozotocin was given for modeling, and fasting blood glucose >16.7 mmol·L~(-1) was considered a sign of successful modeling. The modeling group was randomly divided into the model group, high-, medium-, and low-dose groups of Huanglian Jiedu Decoction, and metformin group. After seven days of intragastric treatment, the feces, colon, and pancreatic tissue of each group of rats were collected, and the pathological changes of the colon and pancreatic tissue of each group were observed by hematoxylin-eosin staining. The changes in the intestinal flora structure of each group were observed by the 16S rRNA sequencing method. The results showed that compared with the model group, the high-, medium-, and low-dose of Huanglian Jiedu Decoction reduced fasting blood glucose levels to different degrees and showed no significant changes in body weight. The number of islet cells increased, and intestinal mucosal damage attenuated. Alpha diversity analysis revealed that Huanglian Jiedu Decoction reduced the abundance and diversity of intestinal flora in rats with T2DM; at the phylum level, low-and mediam-dose of Huanglian Jiedu Decoction reduced the abundance of Bacteroidota, Proteobacteria, and Desulfobacterota and increased the abundance of Firmicute and Bacteroidota/Firmicutes, while the high-dose of Huanglian Jiedu Decoction increased the relative abundance of Proteobacteria and Bacteroidota/Firmicutes ratio, and decreaseal the relative; abundance of Firmicute; at the genus level, Huanglian Jiedu Decoction increased the relative abundance of Allobaculum, Blautia, and Lactobacillus; LEfse analysis revealed that the biomarker of low-and medium-dose groups of Huanglian Jiedu Decoction was Lactobacillus, and the structure of the intestinal flora of the low-dose group of Huanglian Jiedu Decoction was highly similar to that of the metformin group. PICRUSt2 function prediction revealed that Huanglian Jiedu Decoction mainly affected carbohydrate and amino acid metabolic pathways. It suggested that Huanglian Jiedu Decoction could reduce fasting blood glucose and increase the number of islet cells in rats with T2DM, and its mechanism of action may be related to increasing the abundance of short-chain fatty acid-producing strains and Lactobacillus and affecting carbohydrate and amino acid metabolic pathways.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Rats, Sprague-Dawley ; Humans ; Bacteria/drug effects* ; Blood Glucose/metabolism*

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Objective : To investigate the prevalence of negative emotions in hospitalized youth patients with type 2 diabetes(T2DM) and its correlation with coping strategies and psychological resilience. Methods : 141 youth T2DM patients who met the research standards were selected. Blood glucose related indicators, blood pressure, body mass index(BMI), diabetes chronic complications screening results and other data were collected. The basic information and disease related information questionnaire, self-rating depression scale(SDS), self-rating anxiety scale(SAS), diabetes distress scale(DDS), medical coping modes questionnaire(MCMQ) and Connor-Davidson resilience scale(CD-RISC) were completed. Results: Among 141 hospitalized youth T2DM patients, 37.6% were combined with depression, 32.6% were combined with anxiety, and 35.5% were combined with diabetic distress(DD). Univariate analysis showed that systolic blood pressure(P<0.01), educational level, and the form of hospitalization expenses(P<0.05) were significantly correlated with depression. Marital status(P<0.01), family residence, blood glucose monitoring methods, and the last fasting blood glucose(P<0.05) were significantly correlated with anxiety. BMI, whether it was first diagnosed or treated(P<0.01), gender, occupation, disease course, weekly blood glucose monitoring frequency, and the presence of chronic complications(P<0.05) were significantly correlated with DD. In multivariate analysis, systolic blood pressure(P<0.01), educational level, and the form of hospitalization expenses were significantly correlated with depression, marital status(P<0.05) was significantly correlated with anxiety; BMI and weekly blood glucose monitoring frequency(P<0.01) were significantly correlated with DD. SDS, SAS, total scores and dimensions of DDS were negatively correlated with the total score and dimensions of CD-RISC(rs=-0.182--0.467, P<0.05 or 0.01), and positively correlated with the yielding coping strategies(rs=0.177-0.271,P<0.05 or 0.01). SAS,total scores and dimensions of DDS were positively correlated with avoiding coping strategies(rs=0.237-0.419,P<0.05 or 0.01). The total and dimensions of CD-RISC were positively correlated with facing coping strategies(rs=0.215-0.349,P<0.05 or 0.01),and negatively correlated with yielding coping strategies(rs=-0.234--0.325,P<0.01). Conclusion More than 30% of hospitalized youth T2DM may experience negative emotions such as depression,anxiety,and DD. The occurrence of negative emotions in such patients may be related to disease management or socio-economic issues such as systolic blood pressure,educational level,hospitalization expenses,marital status,BMI,and frequency of blood glucose monitoring,as well as decreased psychological resilience and negative coping strategies.

Adaptive immunity is a critical component of the human immune system, playing an essential role in identifying antigens and orchestrating a tailored immune response. This review delves into the significant strides made in the development of epitope prediction tools, their integration into vaccine design, and their pivotal role in enhancing immunotherapy strategies. The review emphasizes the transformative potential of these tools in refining our understanding and application of immune responses. Adaptive immunity distinguishes itself from innate immunity by its ability to recognize specific antigens and remember past infections, leading to quicker and more effective responses upon subsequent exposures. This facet of immunity involves complex interactions between various cell types, primarily B cells and T cells, which recognize distinct epitopes presented by antigens. Epitopes are small sequences or configurations on antigens that are recognized by the immune receptors on B cells and T cells, acting as the focal points of immune recognition and response. Epitopes can be broadly classified into two types: linear (or sequential) epitopes and conformational (or discontinuous) epitopes. Linear epitopes consist of a sequence of amino acids in a protein that are recognized by B cells and T cells in their primary structure form. Conformational epitopes, on the other hand, are formed by spatially distinct amino acids that come together in the tertiary structure of the protein, often recognized by the immune system only when the protein folds into its native conformation. The role of epitopes in the immune response is critical as they are the primary triggers for the activation of B cells and T cells. When an epitope is recognized, it can stimulate B cells to produce antibodies, mobilize helper T cells to secrete cytokines, or prompt cytotoxic T cells to kill infected cells. These actions form the basis of the adaptive immune response, tailored to eliminate specific pathogens or infected cells effectively. The prediction of B cell and T cell epitopes has evolved with advances in computational biology, leading to the development of several sophisticated tools that utilize a variety of algorithms to predict the likelihood of epitope regions on antigens. Tools employing machine learning methods, such as support vector machines (SVMs), XGBoost, random forest, analyze large datasets of known epitopes to classify new sequences as potential epitopes based on their similarity to known data. Moreover, deep learning has emerged as a powerful method in epitope prediction, leveraging neural networks capable of learning high-dimensional data from vast amounts of immunological inputs to identify patterns that may not be evident to other predictive models. Deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and ESM protein language model have demonstrated superior accuracy in mapping the nonlinear relationships inherent in protein structures and epitope interactions. The application of epitope prediction tools in vaccine design is transformative, enabling the development of epitope-based vaccines that can elicit targeted immune responses against specific parts of the pathogen. These vaccines, by focusing the immune response on highly specific regions of the pathogen, can offer high efficacy and reduced side effects. Similarly, in cancer immunotherapy, epitope prediction tools help identify tumor-specific antigens that can be targeted to develop personalized immunotherapeutic strategies, thereby enhancing the precision of cancer treatments. The future of epitope prediction technology appears promising, with ongoing advancements anticipated to enhance the precision and efficiency of these tools further. The integration of broader immunological data, such as patient-specific immune profiles and pathogen variability, along with advances in AI and machine learning, will likely drive the development of more adaptive, robust, and clinically relevant prediction models. This will not only improve the effectiveness of vaccines and immunotherapies but also contribute to our broader understanding of immune mechanisms, potentially leading to breakthroughs in the treatment and prevention of multiple diseases. In conclusion, the development and refinement of epitope prediction tools stand as a cornerstone in the advancement of immunological research and therapeutic design, highlighting a path toward more precise and personalized medicine. The ongoing integration of computational models with experimental immunology holds the promise of revolutionizing our approach to combating infectious diseases and cancer.

We used network pharmacology to predict the mechanism in the treatment of rheumatoid arthritis (RA) via modified Gan Cao Fu Zi Decoction (GCFZ), and validated the results of the analysis and explored the pharmacodynamic effects of GCFZ through animal experiments. Firstly, TCMID, SymMap, HERB, STITCH and GEO databases were utilized to obtain the target genes of GCFZ for the treatment of RA, which yielded a total of 1 250 differentially expressed genes for RA, 534 genes for GCFZ targets and 83 intersecting genes. Then functional enrichment analysis of the intersecting genes was performed through GO and KEGG databases, and the results revealed that GCFZ and its active ingredients mainly functioned through cytokine pathways, where chemokine signaling pathway and tumor necrosis factor (TNF) signaling pathway were enriched with a high number of genes. Cytoscape 3.8.0 software was used to construct the drug-target-disease network and screen key proteins, which included TNF, C-X-C chemokine ligand 8 (CXCL8), C-X-C chemokine ligand 10 (CXCL10), C-C chemokine ligand 5 (CCL5), C-X-C chemokine ligand 2 (CXCL2) and C-X-C chemokine receptor type 4 (CXCR4). The molecular docking technology was used to confirm the binding ability of the main active ingredients of GCFZ to the core proteins. Additionally, the therapeutic effects of GCFZ in low (4 g·kg^-1), medium (8 g·kg^-1) and high (16 g·kg^-1) dose groups were investigated by constructing the collagen-induced arthritis (CIA) rat model. X-ray imaging approach, HE staining and Safranin O-Fast Green staining showed that GCFZ treatment significantly improved bone destruction, synovial hyperplasia and cartilage damage in CIA rats, while immunofluorescence results showed that GCFZ treatment could regulate the expression of TNF, CXCL8 and CCL5. In summary, our results indicate that GCFZ contains a variety of small molecule pharmacodynamic substances, which can exert therapeutic effects via multiple targets and pathways, and obviously reduce the symptoms of arthritis in CIA rats. This animal experiment of our research was approved by the Experimental Animal Management and Ethics Committee of Bengbu Medical College.

Objective: To examine the association of greenness exposure with waist circumference (WC) and central obesity in older adults in China. Methods: Based on the cross-sectional data from the Chinese Longitudinal Healthy Longevity Survey in 2017-2018, 14 056 participants aged 65 years and over were included. Demographic characteristics, lifestyle, WC, and other information were collected through a questionnaire and physical examination. Based on the satellite monitoring data of moderate-resolution imaging spectroradiometer (MODIS) provided by NASA, the annual mean of normalized difference vegetation index (NDVI) within a radius of 1 000 meters was obtained as the measurement value of greenness exposure. Multivariate linear regression model, multivariate logistic regression model, and restricted cubic splines (RCS) model were used to analyze the association and dose-response relationship between greenness exposure and WC and central obesity in older adults in China. Results: A total of 14 056 participants were enrolled with a median age of 84.0 years [IQR: 75.0-94.0 years]. About 45.0% (6 330) of them were male and 48.6% (5 853) were illiterate. There were 10 964 (78.0%) participants from rural. The mean of WC was (84.4±10.8) cm. Central obesity accounted for 60.2% (8 465), and the NDVI range was (-0.06, 0.78). After adjusting for confounding factors, the multivariate linear regression model showed that the change value of WC in the urban group [β (95%CI):-0.49 (-0.93, -0.06)] was smaller than that in the rural [-0.78 (-0.98, -0.58)] for every 0.1 unit increase in NDVI (P_interaction=0.022). Compared with the Q₁ group in NDVI, WC of Q₂ and Q₃ groups in rural decreased, and the β (95%CI) values were-1.74 (-2.5, -0.98) and-2.78 (-3.55, -2.00), respectively. The multivariate logistic regression model showed that after adjusting for confounding factors, the risk of central obesity decreased for urban and rural older adults with an increase of 0.1 unit in NDVI, and the OR (95%CI) values were 0.87 (0.80, 0.95) and 0.86 (0.82, 0.89), respectively (P_interaction=0.284). Compared with the Q₁ group in NDVI, the risk of central obesity in the Q₂ and Q₃ groups in rural was lower, and the OR (95%CI) values were 0.68 (0.58, 0.80) and 0.57 (0.49, 0.68), respectively. The results of the multivariate regression model with RCS showed that there was a non-linear association of NDVI with WC (P_nonlinear=0.006) and central obesity (P_nonlinear=0.025). Conclusion: Greenness exposure is negatively associated with WC and central obesity in older adults in China.

Ionic liquids(ILs) are salts composed entirely of anions and cations in a liquid state at or near room temperature, which have a variety of good physicochemical properties such as low volatility and high stability. This paper mainly reviewed the research overview of ILs in the application of traditional Chinese medicine(TCM) volatile oil preparation technology. Firstly, it briefly introduced the application of TCM volatile oil preparation technology and composition classification and physicochemical properties of ILs, and then summarized the application of ILs in the extraction, separation, analysis, and preparation of TCM volatile oil. Finally, the problems and challenges of ILs in the application of TCM volatile oil were explained, and the application of ILs in TCM volatile oil in the future was prospected.
Ionic Liquids/chemistry* ; Oils, Volatile/analysis* ; Medicine, Chinese Traditional ; Cations ; Biological Products ; Technology

Humans ; Anodontia/genetics* ; Tooth Abnormalities/genetics* ; Mutation ; Malocclusion/genetics* ; Wnt Proteins/genetics*

Objective:To evaluate the value of Sonazoid contrast enhanced ultrasound (CEUS) in preoperative prediction of proliferating cell nuclear antigen 67 (Ki-67) level of hepatocellular carcinoma (HCC) by establishing predictive model based on radiomics features of Kupffer phase.Methods:From October 2020 to August 2021, patients with histologically confirmed HCC lesion and who underwent Sonazoid CEUS examination 1 week before surgery were prospectively enrolled. The radiomics signatures were extracted from the whole tumor region on gray scale images and Kupffer phase images. Two predictive radiomics models were constructed using radiomic method. The predictive performance of 2 models was compared.Results:A total of 50 patients with histologically confirmed single HCC lesions were prospectively enrolled in this study. Among them, histological results revealed 24 HCC lesions with high level representation of Ki-67 (>20%) and 26 HCC lesions with low level representation of Ki-67 (≤20%). Two radiomics predictive models were established based on gray scale images and Kupffer phase images respectively. While compared with model based on B-mode ultrasound images, model based on Kupffer phase images showed significantly higher area under receiver operating characteristic curve (0.753 vs 0.535, P=0.017), accuracy (0.720 vs 0.580, P=0.023) and sensitivity (0.458 vs 0.250, P=0.043). Calibration plot indicated that Kupffer phase model showed better consistency with the actual Ki-67 level than gray scale model. Conclusions:The radiomics model based on Kupffer phase features of Sonazoid CEUS is a preoperative and noninvasive prediction the presentation level of Ki-67 in HCC lesions.