Objective: To investigate the association between monocyte to high-density lipoprotein cholesterol ratio (MHR) and periodontitis and to provide new epidemiologic evidence on the factors affecting periodontitis. Methods: Data on MHR, periodontitis, and other covariates were selected from the NHANES(National Health and Nutrition Examination) database for 3 cycles of subjects in 2009-2010, 2011-2012, and 2013-2014, and a total of 8 456 study subjects were included. The study participants were grouped according to the prevalence of periodontitis (presence or absence), and three regression models (unadjusted covariates, partially adjusted covariates, and fully adjusted covariates) were constructed to analyze the relationship between MHR and periodontitis by using a weighted logistic regression method with stepwise adjustment for confounders. MHR was divided into four groups from Q1 to Q4 according to quartiles from small to large for weighted trend analysis, and the nonlinear relationship between MHR (continuous) and periodontitis was analyzed using a restricted cubic spline with subgroup analysis and sensitivity analysis. Results: All three logistic regression models showed a positive association between MHR and periodontitis (OR = 2.92, 95%CI: 2.14-3.99, P<0.001 (not adjusted); OR = 1.97, 95%CI: 1.39-2.78, P<0.001 (partially adjusted); OR = 1.62, 95%CI: 1.10-2.39, P = 0.017 (fully adjusted)). Trend analysis showed a significantly higher risk of developing periodontitis in the Q4 group compared with the Q1 group in both single (OR = 1.92, 95% CI: 1.58-2.33, P<0.001) and multifactorial analyses (OR = 1.30, 95% CI: 1.03-1.64, P = 0.029). Restricted cubic spline results did not support a nonlinear relationship between MHR and periodontitis (P for nonlinear>0.05), subgroup analysis showed no significant interaction between the covariates and MHR (P>0.05), and sensitivity analysis also showed a positive correlation between MHR and periodontitis (OR = 1.67, 95%CI: 1.31-2.14, P<0.001). Conclusion MHR is positively associated with the risk of developing periodontitis.

OBJECTIVE: To observe the clinical efficacy and safety of acupuncture combined with blade needle therapy for knee osteoarthritis (KOA). METHODS: A total of 60 patients with KOA were randomly divided into an observation group and a control group, 30 cases each group. The control group received acupuncture at Neixiyan (EX-LE4)，Dubi (ST35), Yinlingquan (SP9), Liangqiu (ST34), Xuehai (SP10), Yanglingquan (GB34) and Zusanli (ST36) on the affected side, once every other day, 3 times a week. The observation group received blade needle therapy on the basis of the treatment in the control group, once every 3 days, 2 times a week. Both groups were treated for 4 weeks. Before treatment, after 2, 4 weeks of treatment, and after 1 month of treatment completion (in follow-up), the scores of pain visual analogue scale (VAS), Western Ontario and McMaster Universities osteoarthritis index (WOMAC) and Lequesne index were observed in the two groups, and the clinical efficacy and safety were evaluated. RESULTS: After 2, 4 weeks of treatment and in follow-up, the pain VAS scores, Lequesne index scores, and pain, stiffness, function scores of WOMAC in both groups were decreased compared with those before treatment (P<0.05), and the VAS scores, Lequesne index scores and pain, function scores of WOMAC in the observation group were lower than those in the control group (P<0.05). The effective response rate in the observation group was 76.7% (23/30), while that in the control group was 70.0% (21/30), there was no statistically significant difference in the effective response rates between the two groups (P>0.05). No adverse reactions were observed in either group. CONCLUSION Acupuncture combined with blade needle therapy could alleviate pain and promote functional recovery in KOA patients, and achieve long-lasting improvements.
Humans ; Osteoarthritis, Knee/physiopathology* ; Acupuncture Therapy/instrumentation* ; Male ; Female ; Middle Aged ; Aged ; Acupuncture Points ; Treatment Outcome ; Adult ; Needles ; Combined Modality Therapy

This study employed 16S r RNA gene high-throughput sequencing and metabolomics to explore the mechanism of Angelicae Dahuricae Radix polysaccharides(RP) in the treatment of ulcerative colitis(UC). A mouse model of UC was induced with 2. 5% dextran sulfate sodium. The therapeutic effects of RP on UC in mice were evaluated based on changes in body weight, disease activity index( DAI), and colon length, as well as pathological changes. RT-qPCR was performed to assess the m RNA levels of interleukin(IL)-6, IL-1β, tumor necrosis factor(TNF)-α, myeloperoxidase(MPO), mucin 2(Muc2), Occludin, Claudin2, and ZO-1 in the mouse colon tissue. ELISA was employed to measure the expression of IL-1β and TNF-α in the colon tissue. The intestinal permeability of mice was evaluated by the fluorescent dye permeability assay. Immunohistochemistry was employed to detect the expression of Muc2 and occludin in the colon tissue. Changes in gut microbiota and metabolites were analyzed by 16S r RNA sequencing and ultra-high-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry( UPLC-Q-Exactive Plus Orbitrap MS), respectively. The results indicated that low-dose RP alleviated general symptoms, reduced colonic inflammation and intestinal permeability, and promoted Muc2 secretion and tight junction protein expression in UC mice. In addition, low-dose RP increased gut microbiota diversity in UC mice and decreased the relative abundance of harmful bacteria such as Ochrobactrum and Streptococcus. Twenty-seven differential metabolites were identified in feces, and low-dose RP restored the levels of disturbed metabolites. Notably, arginine and proline metabolism were the most significantly altered amino acid metabolic pathways following lowdose RP intervention. In conclusion, RP can ameliorate general symptoms, inhibit colonic inflammation, and maintain intestinal mucosal barrier integrity in UC mice by modulating gut microbiota composition and arginine and proline metabolism.
Animals ; Gastrointestinal Microbiome/drug effects* ; Colitis, Ulcerative/genetics* ; Mice ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Polysaccharides/administration & dosage* ; Angelica/chemistry* ; Humans ; Colon/metabolism* ; Disease Models, Animal ; Mucin-2/metabolism* ; Tumor Necrosis Factor-alpha/metabolism*

BACKGROUND: The atherogenic index of plasma (AIP) has been shown to be positively correlated with cardiovascular disease in previous studies. However, it is unclear whether elderly people with long-term high AIP levels are more likely to develop coronary heart disease (CHD). Therefore, the aim of this study was to investigate the relationship between AIP trajectory and CHD incidence in elderly people. METHODS: 19,194 participants aged ≥ 60 years who had three AIP measurements between 2018 and 2020 were included in this study. AIP was defined as log₁₀ (triglyceride/high-density lipoprotein cholesterol). The group-based trajectory model was used to identify different trajectory patterns of AIP from 2018 to 2020. Cox proportional hazards models were used to estimate the hazard ratio (HR) with 95% CI of CHD events between different trajectory groups from 2020 to 2023. RESULTS: Three different trajectory patterns were identified through group-based trajectory model: the low-level group (n = 7410, mean AIP: -0.25 to -0.17), the medium-level group (n = 9981, mean AIP: 0.02-0.08), and the high-level group (n = 1803, mean AIP: 0.38-0.42). During a mean follow-up of 2.65 years, a total of 1391 participants developed CHD. After adjusting for potential confounders, compared with the participants in the low-level group, the HR with 95% CI of the medium-level group and the high-level group were estimated to be 1.24 (1.10-1.40) and 1.43 (1.19-1.73), respectively. These findings remained consistent in subgroup analyses and sensitivity analyses. CONCLUSIONS There was a significant correlation between persistent high AIP level and increased CHD risk in the elderly. This suggests that monitoring the long-term changes in AIP is helpful to identify individuals at high CHD risk in elderly people.

Alzheimer's disease (AD) remains a formidable challenge in modern healthcare, necessitating innovative approaches for its early detection and intervention. This study aimed to enhance the identification of individuals with mild cognitive impairment (MCI) at risk of developing AD. Leveraging advances in computational power and the extensive availability of healthcare data, we explored the potential of deep learning models for early prediction using medical claims data. We employed a bidirectional gated recurrent unit (BiGRU) deep learning model for predictive modeling of MCI progression across various prediction intervals, extending up to five years post-initial MCI diagnosis. The performance of the BiGRU model was rigorously compared with several machine-learning model baselines to evaluate its efficacy. Using a robust cross-validation methodology, the BiGRU emerged as the top-performing model, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.833 (95% CI: 0.822, 0.843), an Area Under the Precision-Recall Curve (AUC-PR) of 0.856 (95% CI: 0.845, 0.867), and an F1-Score of 0.71 (95% CI: 0.694, 0.724) for a five-year prediction interval. The results indicate that BiGRU, utilizing longitudinal claims data, reliably predicts MCI-to-AD progression over a lengthy interval following the initial MCI diagnosis, offering clinicians a valuable tool for targeted risk identification and stratification.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional