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.

Humans ; Melphalan/therapeutic use* ; Multiple Myeloma/therapy* ; Hematopoietic Stem Cell Transplantation ; Transplantation, Autologous ; Transplantation Conditioning

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 compare the supply data of red blood cells(RBCs) from 18 blood centers in China before and after the outbreak of COVID-19 during 2018 to 2021. 【Methods】 Eight indicators related to RBCs supply from 18 blood centers in China during 2018-2021 were collected retrospectively, including the storage of total amount of qualified RBCs (referred to as the total amount of storage), the distribution of total amount of RBCs (referred to as the total amount of distribution), the distribution amount of RBCs per 1 000 population (referred to as the amount of distribution per 1 000 population), the distribution amount of RBCs from 400 mL original blood per 1 000 population [referred to as the amount of distribution per 1 000 population (400 mL)], the average daily distribution amount of RBCs (referred to as the average daily distribution amount), the average daily storage amount of RBCs (referred to as the average daily storage amount), the average storage days of RBCs when distribute (referred to as the RBC storage days), and the expired amount of RBCs (referred to as the expired amount). Based on the outbreak time of COVID-19, the data of 2018 and 2019 were the pre-pandemic group, and the data of 2020 and 2021 were the post-pandemic group. 【Results】 Data on RBCs supply in 18 blood centers from 2018 to 2021(comparison of the pre-pandemic group and the post-pandemic group): the amount of distribution per 1 000 population (median 14.68 U>13.92 U) decreased, the amount of distribution per 1 000 population (400 mL) (median 10.16 U>9.21 U) decreased, and the difference was statistically significant (P<0.05); data comparison between 2019 and 2020:the total amount of distribution (median 117 770.38 U>99 084.08 U) decreased, the amount of distribution per 1 000 population (median 15.04 U>12.19 U) decreased, the amount of distribution per 1000 population (400 mL) (median 10.11 U>8.94 U), the average daily distribution amount(322.66 U>270.73 U) decreased and RBC storage days (median 10.50 d<11.45 d) increased, the difference has statistical significance (P<0.05); data comparison between 2020 and 2021:the total amount of storage (median 101 920.25 U<120 328.63 U), the total amount of distribution (median 99 084.08 U<118 428.62 U), the amount of distribution per 1 000 population (median 12.19 U<15.00 U), the amount of distribution per 1 000 population (400 mL) (median 8.94 U<9.46 U), the average daily distribution amount (270.73 U>324.46 U), the average daily inventory (median 3 222.00 U<4 328.00 U) increased, the expired amount (median 1.50 U>0.00 U) decreased, the difference has statistical significance (P<0.05). The results of ANOVA showed that there were significant differences on the data related to RBCs supply (except expired amount) in different blood centers (P<0.05). The ratio of average daily stock to average daily distribution in the post-outbreak group (median 12.36 d) was higher than that in the pre-outbreak group (median 10.92 d), the difference has statistical significance (P<0.05), with significant difference among different blood centers (P <0.05). 【Conclusion】 The COVID-19 pandemic has a significant impact on RBCs supply in different blood centers. In the second year of the pandemic, the supply capability had recovered to some extent, and there were differences in RBCs supply in different blood centers.