Objective To investigate the epidemiological characteristics of malaria and provide a basis for developing improved prevention and control measures. Methods Data were obtained from the Chinese Disease Prevention and Control Information System. Malaria surveillance data for Nanning City from January 1,2023, to December 31,2024, were exported from the Infectious Disease Reporting Information Management Subsystem. The characteristics of the two groups of malaria cases were compared. Results A total of 103 imported malaria cases were reported in Nanning City in 2024, representing a 38.32% decrease compared with the same period of the previous year. No statistically significant difference were observed between cases reported in 2023 and 2024 in terms of average age, gender ratio, proportion of parasite species, and monthly reporting distribution;however, statistically significant differences were found in the proportion of reporting areas and current residence areas(χ2= 13.572 and 10.355, respectively; P = 0.001 and 0.035, respectively). The proportion of cases reported in Shanglin County and the proportion of cases residing in Shanglin County were both lower than those during the same period of the previous year. Conclusions The high aggregation of imported malaria cases in Nanning City has decreased. Medical institutions in areas other than Shanglin County should strengthen their vigilance against malaria.

Systemic lupus erythematosus (SLE) is an autoimmune disease of unknown etiology, primarily characterized by systemic inflammation and hyperactivation of both B and T lymphocytes. Key immunological features include increased consumption of complement components, sustained overproduction of type I interferons (IFN-I), and persistent production of a broad spectrum of autoantibodies, such as anti-dsDNA antibodies. However, the use of autoantibodies as biomarkers for the early detection of SLE is associated with a high false-positive rate, suggesting that antibody characteristics evolve during disease progression.N-glycosylation is a critical post-translational modification of antibodies that significantly influences their structure and receptor-binding properties, thereby modulating biological activities and functions. In particular, glycosylation patterns affect the antibody’s affinity for Fc gamma receptors (FcγRs), subsequently regulating various antibody-mediated immune responses. Numerous studies have investigated the impact of individual monosaccharides—such as sialic acid, fucose, and N-acetylglucosamine, which constitute N-glycans—on the immunological functions of antibodies. This review systematically summarizes the aberrant immunoglobulin G (IgG) N-glycosylation patterns observed in SLE patients, with a focus on correlations between disease progression or complications and quantitative alterations in individual glycan components. We first review how different types of N-glycosylation modifications affect the biological activity and functional properties of IgG, particularly regarding the effects of specific monosaccharides—such as sialic acid, fucose, and galactose—on FcγR binding affinity and the resulting downstream immune functions. We then summarize the differential expression of IgGN-glycans and glycosyltransferase genes between SLE patients and healthy controls, and outline the associations between glycosylation changes and SLE-related pathological responses. In response to the inconsistencies and limitations in current research, we propose potential explanations from the perspectives of study methodologies, participant characteristics, and variations in N-glycan structures, aiming to provide a constructive reference for future studies. Given the close relationship between antibody glycosylation and SLE, this review highlights the potential of IgG N-glycosylation patterns as promising biomarkers for early diagnosis and disease monitoring. In terms of therapy, we discuss how IgG glycosylation can enhance the efficacy of intravenous immunoglobulin (IVIg) treatment and introduce emerging therapeutic strategies that aim to modulate endogenous IgG N-glycans as a novel glycan-based approach for SLE management. In summary, N-glycans are essential structural components of antibodies that regulate immune responses by modulating antibody-receptor interactions. Aberrant glycosylation is closely associated with the pathogenesis of autoimmune diseases, including SLE. However, due to the structural diversity of N-glycans and the complexity of glycosylation processes, the precise roles of IgGN-glycosylation in SLE pathophysiology remain incompletely understood. Moreover, therapeutic strategies targeting IgG glycosylation are still in early development and have not yet reached clinical application. Continued progress in glycan analysis technologies and other biological tools, along with interdisciplinary collaboration, will be essential for advancing this field.

Human milk is universally recognized as the optimal and most natural source of nutrition for newborns, offering benefits that extend far beyond basic energy and macronutrient provision. Among its complex constituents, human milk oligosaccharides (HMOs) represent the third most abundant solid component, surpassed only by lactose and lipids. HMOs are distinguished by their exceptionally high structural diversity—over 200 distinct structures have been identified to date. This structural complexity underlies the extensive biological functions HMOs perform within the infant’s body. HMOs play a pivotal role in promoting healthy growth, development, and overall well-being in infants and young children, functioning as indispensable bioactive molecules. Their key physiological activities include: immunomodulation and allergy prevention by promoting immune tolerance and reducing the risk of allergic diseases; potent anti-inflammatory and antioxidant effects that protect vulnerable infant tissues; support for brain development and cognitive enhancement through multiple mechanisms; anti-pathogenic properties, acting as soluble receptor analogs or “decoy” molecules to competitively block viral, bacterial, and other pathogen adhesion, thereby preventing colonization and infection in the gastrointestinal tract; and functioning as blood group substances. At the translational and application level, HMO research is actively driving cross-disciplinary innovation. Building on a deep understanding of their immunological and neurodevelopmental benefits, certain structurally defined HMOs have been successfully incorporated into infant formula. These HMO-supplemented formulas have received regulatory approval and are now commercially available worldwide, providing a nutritional alternative that more closely resembles human milk for infants who are not exclusively breastfed. This represents a significant step toward narrowing the compositional gap between formula and breast milk. Simultaneously, research into the symbiotic relationship between HMOs and the gut microbiota—particularly their role as selective prebiotic substrates promoting the growth of beneficial bacteria—has catalyzed the development of novel functional foods, dietary supplements, and microbiome-targeted therapies. These include advanced synbiotic formulations that combine specific probiotic strains with HMOs to synergistically optimize gut health and function. Furthermore, the intrinsic qualities of HMOs—including their natural origin, safety profile, biocompatibility, and proven antioxidant properties—have attracted growing interest in the emerging field of high-performance cosmetics. They are increasingly being explored as innovative functional ingredients in skincare products aimed at reducing oxidative stress and supporting skin health. This review aims to systematically synthesize recent advancements in HMO research, offering a comprehensive analysis centered on their complex composition and structural diversity; the molecular and cellular mechanisms underlying their diverse biological functions; their translational potential across sectors such as nutrition, medicine, and consumer care (including cosmetics); and the major challenges that persist in the field. It critically examines both foundational discoveries and recent breakthroughs. By integrating these interconnected themes, the review provides a holistic and up-to-date perspective on the scientific landscape of HMOs, highlighting their essential role in early-life nutrition and their expanding relevance across health and wellness applications. It also outlines promising directions for future research, with the goal of advancing evidence-based innovation in infant health and beyond.

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

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

This research study focuses on addressing the limitations of current neuropathic pain (NP) treatments by developing a novel dual-target modulator, E0199, targeting both Na_V1.7, Na_V1.8, and Na_V1.9 and K_V7 channels, a crucial regulator in controlling NP symptoms. The objective of the study was to synthesize a compound capable of modulating these channels to alleviate NP. Through an experimental design involving both in vitro and in vivo methods, E0199 was tested for its efficacy on ion channels and its therapeutic potential in a chronic constriction injury (CCI) mouse model. The results demonstrated that E0199 significantly inhibited Na_V1.7, Na_V1.8, and Na_V1.9 channels with a particularly low half maximal inhibitory concentration (IC₅₀) for Na_V1.9 by promoting sodium channel inactivation, and also effectively increased K_V7.2/7.3, K_V7.2, and K_V7.5 channels, excluding K_V7.1 by promoting potassium channel activation. This dual action significantly reduced the excitability of dorsal root ganglion neurons and alleviated pain hypersensitivity in mice at low doses, indicating a potent analgesic effect without affecting heart and skeletal muscle ion channels critically. The safety of E0199 was supported by neurobehavioral evaluations. Conclusively, E0199 represents a ground-breaking approach in NP treatment, showcasing the potential of dual-target small-molecule compounds in providing a more effective and safe therapeutic option for NP. This study introduces a promising direction for the future development of NP therapeutics.

Neddylation is a crucial posttranslational modification that involves the attachment of neural precursor cell-expressed developmentally downregulated protein 8 (NEDD8) to a lysine residue in the substrate via the sequential actions of the E1 NEDD8-activating enzyme (NAE) (E1), E2 NEDD8-conjugating enzyme (E2), and E3 NEDD8-ligase (E3). The most extensively studied substrates of neddylation are members of the cullin family, which act as scaffold components for cullin ring E3 ubiquitin ligases (CRLs). Since cullin neddylation activates CRLs, which are frequently overactive in tumors, inhibiting neddylation has emerged as a promising strategy for developing novel antitumor therapies. This review explores the antitumor effects of inhibiting neddylation that leads to the inactivation of CRLs and provides a summary of known inhibitors that target protein-protein interactions (PPIs) within the neddylation enzymatic cascade.

Progressive pulmonary fibrosis (PPF) is a progressive and lethal condition with few effective treatment options. Improvements in quality of life for patients with PPF remain limited even while receiving treatment with approved antifibrotic drugs. Traditional Chinese medicine (TCM) has the potential to improve cough, dyspnea and fatigue symptoms of patients with PPF. TCM treatments are typically diverse and individualized, requiring urgent development of efficient and precise design strategies to identify effective treatment options. We designed an innovative Bayesian adaptive two-stage trial, hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF. An open-label, two-stage, adaptive Bayesian randomized controlled trial will be conducted in China. Based on Bayesian methods, the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial. The adaptive Bayesian trial design will employ a Bayesian hierarchical model with "stopping" and "continuation" criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached. The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented. The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score, reflecting an improvement in cough-specific quality of life. The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF, and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases. However, due to the complexity of the trial implementation, sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response. Moreover, detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. Please cite this article as: Zhang C, Nie YS, Zhang CT, Yang HJ, Zhang HR, Xiao W, Cui GF, Li J, Li SJ, Huang QS, Yan SY. An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design. J Integr Med. 2025; 23(2): 138-145.
Female ; Humans ; Male ; Bayes Theorem ; Disease Progression ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Pulmonary Fibrosis/therapy* ; Quality of Life ; Randomized Controlled Trials as Topic ; Research Design ; Adaptive Clinical Trials as Topic

Objective:To establish machine learning models to predict visual improvement and anatomical response after anti-vascular endothelial growth factor (VEGF) treatment in patients with diabetic macular edema (DME).Methods:A multi-algorithm machine learning predictive modeling study based on retrospective clinical data was conducted.A total of 225 patients with DME who received their first intravitreal anti-VEGF injection at Fuzhou University Affiliated Provincial Hospital were enrolled between January 2023 and April 2025.According to data completeness, 204 cases were included in the visual recovery prediction model and 201 cases were included in the anatomical response prediction model.Baseline data included optical coherence tomography (OCT) features and blood biomarkers.The primary outcomes were defined as an improvement of ≥1 line in visual acuity and a reduction of ≥20% in central retinal thickness (CRT) after anti-VEGF treatment.Feature selection was performed using univariate logistic regression and Lasso regression.Four machine learning algorithms, logistic regression (LR), decision tree, multilayer perceptron, and random forest, were trained and validated.Model performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis.The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (No.K2025-03-064).Written informed consent was obtained from each subject.Results:Among the 37 baseline variables, five key predictors were identified for the outcome of ≥20% CRT reduction: baseline CRT, baseline CRT ≥400 μm, presence of subretinal fluid (SRF), disorganization of the retinal inner layers (DRIL), and integrity of the ellipsoid zone (EZ).Among the four models, the LR model had the best performance, with an accuracy of 0.88, sensitivity of 0.94, specificity of 0.70, and an AUC of 0.94 (95% confidence interval [ CI]: 0.87-1.00).SHAP analysis showed that baseline CRT ≥400 μm, DRIL, SRF, and baseline CRT contributed positively to the outcome, while EZ integrity was a negative predictor for CRT reduction.For the outcome of ≥1-line visual improvement, two key predictors were identified: baseline best corrected visual acuity (BCVA) and EZ integrity.Both baseline BCVA and EZ integrity were negative predictors for ≥1-line visual improvement.The LR model also had the best performance in the internal validation cohort, with an accuracy of 0.71, sensitivity of 0.67, specificity of 0.75, and an AUC of 0.76 (95% confidence interval [ CI]: 0.61-0.91).A visual nomogram was developed based on the selected predictors and the best-performing model.By converting patient-specific clinical characteristics into scores, clinicians can calculate a total score and estimate the probability of achieving a reduction of ≥20% in CRT and a ≥1-line improvement in visual acuity after anti-VEGF therapy. Conclusions:Machine learning-based model building can effectively predict visual and anatomical response following anti-VEGF treatment in DME patients.Logistic regression shows robust predictive performance for both outcomes.Identification of key predictors, especially OCT features such as EZ integrity, SRF, and DRIL, may aid in guiding treatment expectation assessment and personalized intervention strategies.Nomogram constructed in this study shows good clinical applicability and may serve as a decision-support tool to improve the precision of DME management.