Traditional Chinese medicine (TCM) is a well-accepted therapy for atopic dermatitis (AD). However, there are currently no evidence-based guidelines integrating TCM and Western medicine for the treatment of AD, limiting the clinical application of such combined approaches. Therefore, the China Association of Chinese Medicine initiated the development of the current guideline, focusing on key issues related to the use of TCM in the treatment of AD. This guideline was developed in accordance with the principles of the guideline formulation manual published by the World Health Organization. A comprehensive review of the literature on the combined use of TCM and Western medicine to treat AD was conducted. The findings were extensively discussed by experts in dermatology and pharmacy with expertise in both TCM and Western medicine. This guideline comprises 23 recommendations across seven major areas, including TCM syndrome differentiation and classification of AD, principles and application scenarios of TCM combined with Western medicine for treating AD, outcome indicators for evaluating clinical efficacy of AD treatment, integration of TCM pattern classification and Western medicine across disease stages, daily management of AD, the use of internal TCM therapies and proprietary Chinese medicines, and TCM external treatments. Please cite this article as: Du XR, Wu MY, Tao MC, Lin Y, Gu CY, Wu MF, Cao Y, Chen DC, Li W, Wang HW, Wang Y, Wang Y, Lu HZ, Liu X, Su XF, Li FL. Clinical practice guidelines for the diagnosis and treatment of atopic dermatitis with integrative traditional Chinese and Western medicine. J Integr Med. 2025; 23(6):641-653.
Dermatitis, Atopic/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Integrative Medicine ; Drugs, Chinese Herbal/therapeutic use* ; Practice Guidelines as Topic

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective China is among the 30 countries with a high burden of tuberculosis(TB)worldwide,and TB remains a public health concern.Kashgar Prefecture in the southern Xinjiang Autonomous Region is considered as one of the highest TB burden regions in China.However,molecular epidemiological studies of Kashgar are lacking. Methods A population-based retrospective study was conducted using whole-genome sequencing(WGS)to determine the characteristics of drug resistance and the transmission patterns. Results A total of 1,668 isolates collected in 2020 were classified into lineages 2(46.0%),3(27.5%),and 4(26.5%).The drug resistance rates revealed by WGS showed that the top three drugs in terms of the resistance rate were isoniazid(7.4%,124/1,668),streptomycin(6.0%,100/1,668),and rifampicin(3.3%,55/1,668).The rate of rifampicin resistance was 1.8%(23/1,290)in the new cases and 9.4%(32/340)in the previously treated cases.Known resistance mutations were detected more frequently in lineage 2 strains than in lineage 3 or 4 strains,respectively:18.6%vs.8.7 or 9%,P<0.001.The estimated proportion of recent transmissions was 25.9%(432/1,668).Multivariate logistic analyses indicated that sex,age,occupation,lineage,and drug resistance were the risk factors for recent transmission.Despite the low rate of drug resistance,drug-resistant strains had a higher risk of recent transmission than the susceptible strains(adjusted odds ratio,1.414;95%CI,1.023-1.954;P = 0.036).Among all patients with drug-resistant tuberculosis(DR-TB),78.4%(171/218)were attributed to the transmission of DR-TB strains. Conclusion Our results suggest that drug-resistant strains are more transmissible than susceptible strains and that transmission is the major driving force of the current DR-TB epidemic in Kashgar.

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.

AIM To study the chemical constituents from the leaves of Citrus reticulata Blanco and their anti-inflammatory activities.METHODS The 85%ethanol extract from the leaves of C.reticulata was isolated and purified by silica gel,D101 macroporous resin,MCI,ODS and Sephadex LH-20,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The Griess method was used to determine their inhibitory activities on lipopolysaccharide-induced NO production in macrophages RAW 264.7 cells.The mice foot swelling inflammation model induced by carrageenan was established,and the levels of IL-1β,TNF-α were detected.RESULTS Twelve compounds were isolated and identified as nobiletin(1),tangeretin(2),5-demethylinoblitin(3),5,4'-dihydroxy-6,7,8,3'-tetramethoxy flavone(4),5-hydroxy-7,8,3',4'-trimethoxyflavanone(5),3,5,6,7,8,3',4'-heptamethoxyflavanone(6),hesperetin(7),5-hydroxy-6,7,3',4'-tetramethoxyphenone(8),β-balsam alcohol(9),stigmaster-5-en-3β-alcohol(10),p-hydroxybenzaldehyde(11),vanillin(12).Compounds 1,4,6,7,10 and 12 had strong inhibitory activites on NO release in LPS-induced RAW 264.7 cells,and the IC50 values were(25.21±2.10),(37.77±0.50),(38.19±1.58),(21.89±1.73),(43.81±1.18),(47.98±2.55),(41.23±1.11),(43.80±1.43)μmol/mL,respectively.Compounds 2-3 reduced IL-1β and TNF-α levels(P＜0.05,P＜0.01).CONCLUSION Compounds 6-7,9 are isolated from this plant for the first time.Compounds 1-4,8 exhibit strong in vitro anti-inflammatory activities,and compounds 2-3 exhibit significant in vivo anti-inflammatory activities.

This is the first reported case of a female with monkeypox infection in Kunming City,Yunnan Province.An epi-demiological investigation was conducted to provide a scientific basis for the prevention and control of monkeypox epidemics in China,especially for early detection in females in accordance with the"Monkeypox prevention and control program(2023 ver-sion)".Diagnosis was performed as described in the"Monkeypox Diagnosis and Treatment Guidelines(2022 version)".Speci-mens were collected for laboratory testing.The epidemiological investigation determined that the female patient had sexual in-tercourse with her newly married husband once before disease onset and the husband hid his history of male homosexual sex.The laboratory test results of the woman and her husband were positive for the nucleic acid of the monkeypox virus.Both had typical clinical symptoms,including rash.The epidemiological investigation,clinical symptoms,laboratory test results,and previous epidemic data of monkeypox in Yunnan province confirmed the woman as the first female infected with monkeypox in Yunnan Province and her husband was the presumed source of infection.

The purpose of this research is to identify the chemical constituents of sea buckthorn leaves extract (SBLE) and explore its hypoglycemic biological activity. SBLE was prepared by hot reflux extraction with 65% ethanol, and its chemical composition was analyzed by ultra-high-performance liquid chromatography-photodiode array-mass spectrometry/mass spectrometry (UHPLC-PDA-MS/MS) system. The animal experiments were compliant with ethical principles for animal use and had been approved by the Animal Experiment Ethics Committee of Jinan University. Mice were injected with streptozocin (STZ) to establish a hyperglycemic animal model, and SBLE (1.5 g·kg^-1) was administered by gavage for 5 weeks. The fasting blood glucose (FBG) and oral glucose tolerance were detected. Normal mice were given SBLE (1.5 g·kg^-1) by intragastric administration for 10 days, and blood was collected from the tail vein to detect the changes in blood glucose within 120 min after sucrose or starch loading. The mucous membrane of the small intestine of mice was taken to detect the activity of α-glucosidase (AG), and the activity of yeast-derived AG incubated with SBLE was evaluated. The glucose uptake by Caco-2 cells treated with SBLE was detected by fluorescence microscopy and cytometry, and the gene expression of sodium-dependent glucose transporter 1 (SGLT1) and glucose transporter 2 (GLUT2) in Caco-2 cells were detected by real-time quantitative PCR (qPCR). A total of 18 compounds were identified, mainly including tannins and flavonoids. SBLE reduced FBG and increased oral glucose tolerance in STZ hyperglycemic mice. SBLE effectively inhibited the increase of blood glucose caused by starch intake in normal mice. SBLE exerted good inhibitory activity on yeast-derived AG (IC₅₀ = 16.94 μg·mL^-1) and small intestinal mucosa AG with an inhibition rate of 15.48%. SBLE (25-100 μg·mL^-1) dose-dependently inhibited glucose uptake by Caco-2 cells, and SBLE significantly reduced the mRNA level of SGLT1 without changing the expression of GLUT2. In conclusion, the UHPLC characteristic fingerprint of SBLE is established with 18 chemical components identified by mass spectrometry, and SBLE exerts hypoglycemic effect by inhibiting the activity of AG and the absorption of glucose by intestinal epithelial cells.

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

Drug-induced liver injury (DILI) risk prediction, diagnosis establishment, clinical management, and all other aspects are facing great challenges. Although the current understanding of its pathogenesis is still incomplete, research over the past 20 years has shown that genetic susceptibility may play an important role in the occurrence and development of DILI. In recent years, pharmacogenomics studies have further revealed the association between human leukocyte antigen (HLA) genes, some non-HLA genes, and hepatotoxicity from certain drugs. However, due to the lack of well-designed, prospective, large-sample cohort validation and low positive predictive values, there may still be some way to go before the current results can be truly translated into clinical practice for precise prediction and prevention of DILI risk.
Humans ; Genetic Predisposition to Disease ; Prospective Studies ; Risk Factors ; Chemical and Drug Induced Liver Injury/genetics* ; Drug-Related Side Effects and Adverse Reactions ; Liver

OBJECTIVES: To explore the relationship between atherogenic index of plasma (AIP) and childhood asthma. METHODS: This retrospective study included 86 children with asthma admitted to the Changzhou Second People's Hospital Affiliated to Nanjing Medical University from July 2020 to August 2022 as the asthma group and 149 healthy children undergoing physical examination during the same period as the control group. Metabolic parameters including total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and blood glucose, as well as general information of the children such as height, weight, body mass index, presence of specific dermatitis, history of inhalant allergen hypersensitivity, family history of asthma, and feeding history, were collected. Multivariable logistic regression analysis was used to study the relationship between AIP, triglycerides, and high-density lipoprotein cholesterol and asthma. The value of AIP, triglycerides, and high-density lipoprotein cholesterol for predicting asthma was assessed using receiver operating characteristic (ROC) curve analysis. RESULTS: The AIP and triglyceride levels in the asthma group were significantly higher than those in the control group, while high-density lipoprotein cholesterol was significantly lower (P<0.05). However, there was no significant difference in total cholesterol and low-density lipoprotein cholesterol between the two groups (P>0.05). Before and after adjusting for height, weight, presence of specific dermatitis, history of inhalant allergen hypersensitivity, family history of asthma, feeding method, and blood glucose, multivariable logistic regression analysis showed that AIP, triglycerides, and high-density lipoprotein cholesterol were associated with asthma (P<0.05). ROC curve analysis showed that the optimal cutoff value for predicting asthma with AIP was -0.333, with a sensitivity of 80.2%, specificity of 55.0%, positive predictive value of 50.71%, and negative predictive value of 82.85%. The area under the curve (AUC) for AIP in predicting asthma was significantly higher than that for triglycerides (P=0.009), but there was no significant difference in AUC between AIP and high-density lipoprotein cholesterol (P=0.686). CONCLUSIONS AIP, triglycerides, and high-density lipoprotein cholesterol are all associated with asthma. AIP has a higher value for predicting asthma than triglycerides and comparable value to high-density lipoprotein cholesterol.
Humans ; Child ; Retrospective Studies ; Blood Glucose ; Triglycerides ; Cholesterol, HDL ; Cholesterol, LDL ; Asthma/etiology* ; Dermatitis ; Risk Factors